The Effect of Social Media Education in Preventing Smoking in Adolescents to Reduce the Incidence of COPD

Objective: Smoking is the leading cause of chronic obstructive pulmonary disease (COPD) and can lead to other health problems like cardiovascular disease, type two diabetes, and lung cancer (World Health Organization, 2024). The population chosen for the study is adolescents aged 10-19 because smokers normally start smoking on a routine basis before the age of twenty (Reitsma, 2021). The research question is: what is the effect of social media education in preventing smoking in adolescents to reduce the incidence of COPD? Methods: A literature review was conducted to explore evidence-based best practices for social media education using ProQuest and Google Scholar search engines. A boolean strategy was used with the word “AND” along with the following keywords: quantitative study, smoking prevention, adolescents, and using social media. The search results were narrowed using the following filters: meta-analysis, systematic reviews, full text, peer-reviewed, last five years, and evidence-based healthcare. Results: Four articles were chosen based on the highest levels of evidence. Two systematic review articles and two randomized control trials were reviewed. One systematic review focused on social media education in adults, which is shown to be an effective tool for smoking prevention. The second systematic review found that a school-based smoking prevention education program was successful for adolescent education. The remaining two randomized control trials supported social media education in adolescents to prevent smoking. Conclusion: The literature revealed social media interventions are cost-effective that can reach larger target audiences. The literature review provides supporting evidence that social media education programs about smoking prevention in adolescents should be adopted. However, additional research is recommended to examine how social media education impacts smoking patterns in the long term and its relationship to the possible development of COPD, as well as to determine the best social media platform to use for education

Assessing the impact of climate change on the cost of production of green ammonia from offshore wind

Green ammonia has received significant interest as a zero-carbon energy vector. However, current techno-economic models used to estimate the cost of producing green ammonia only use historical weather datasets as their inputs. Climate change is beginning to have an observable impact on global weather systems, so it is therefore important to examine how resilient locations for green ammonia production will be to the effects of climate change on renewable energy resources. This work examines how the cost of producing green ammonia from offshore wind farms at four locations in the UK could change due to climate change. It uses the 1981–2000, 2021–2040 and 2061–2080 2.2km projections under the RCP8.5 scenario from the Met Office's UK Climate Projections 2018 dataset, which were bias corrected with reference to the ERA5 reanalysis dataset. Using an islanded green ammonia production model, the achievable levelised cost of ammonia (LCOA) was evaluated at four sites taken from confirmed projects in the UK's Offshore Wind Licensing Round 4, with the achievable LCOAs found to range between 935 and 1696 USD/t. Results from the three time periods were compared to assess the impact of climate change and were benchmarked against LCOAs from conventional production pathways. At all sites, increases of between 6% and 8% of the average LCOAs were observed for the 2021–2040 and 2061–2080 scenarios respectively, with the changes found to be statistically significant through application of a two tailed T-test with a confidence level of 5%

Event attribution of a midlatitude windstorm using ensemble weather forecasts

The widespread destruction incurred by midlatitude storms every year makes it an imperative to study how storms change with climate. The impact of climate change on midlatitude windstorms, however, is hard to evaluate due to the small signals in variables such as wind speed, as well as the high resolutions required to represent the dynamic processes in the storms. Here, we assess how storm Eunice, which hit the UK in February 2022, was impacted by anthropogenic climate change using the ECMWF ensemble prediction system. This system was demonstrably able to predict the storm, significantly increasing our confidence in its ability to model the key physical processes and their response to climate change. Using modified greenhouse gas concentrations and changed initial conditions for ocean temperatures, we create two counterfactual scenarios of storm Eunice in addition to the forecast for the current climate. We compare the intensity and severity of the storm between the pre-industrial, current, and future climates. Our results robustly indicate that Eunice has become more intense with climate change and similar storms will continue to intensify with further anthropogenic forcing. These results are consistent across forecast lead times, increasing our confidence in them. Analysis of storm composites shows that this process is caused by increased vorticity production through increased humidity in the warm conveyor belt of the storm. This is consistent with previous studies on extreme windstorms. Our approach of combining forecasts at different lead times for event attribution enables combining event specificity and a focus on dynamic changes with the assessment of changing risks from windstorms. Further work is needed to develop methods to adjust the initial conditions of the atmosphere for the use in attribution studies using weather forecasts but we show that this approach is viable for reliable and fast attribution systems

A comparison of model ensembles for attributing 2012 West African rainfall

In 2012, heavy rainfall resulted in flooding and devastating impacts across West Africa. With many people highly vulnerable to such events in this region, this study investigates whether anthropogenic climate change has influenced such heavy precipitation events. We use a probabilistic event attribution approach to assess the contribution of anthropogenic greenhouse gas emissions, by comparing the probability of such an event occurring in climate model simulations with all known climate forcings to those where natural forcings only are simulated. An ensemble of simulations from 10 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) is compared to two much larger ensembles of atmosphere-only simulations, from the Met Office model HadGEM3-A and from weather@home with a regional version of HadAM3P. These are used to assess whether the choice of model ensemble influences the attribution statement that can be made. Results show that anthropogenic greenhouse gas emissions have decreased the probability of high precipitation across most of the model ensembles. However, the magnitude and confidence intervals of the decrease depend on the ensemble used, with more certainty in the magnitude in the atmosphere-only model ensembles due to larger ensemble sizes from single models with more constrained simulations. Certainty is greatly decreased when considering a CMIP5 ensemble that can represent the relevant teleconnections due to a decrease in ensemble members. An increase in probability of high precipitation in HadGEM3-A using the observed trend in sea surface temperatures (SSTs) for natural simulations highlights the need to ensure that estimates of natural SSTs are consistent with observed trends in order for results to be robust. Further work is needed to establish how anthropogenic forcings are affecting the rainfall processes in these simulations in order to better understand the differences in the overall effect

Falling Snow Estimates from the Global Precipitation Measurement (GPM) Mission

Retrievals of falling snow from space represent an important data set for understanding the Earth's atmospheric, hydrological, and energy cycles, especially during climate change. Estimates of falling snow must be captured to obtain the true global precipitation water cycle, snowfall accumulations are required for hydrological studies, and without knowledge of the frozen particles in clouds one cannot adequately understand the energy and radiation budgets. While satellite-based remote sensing provides global coverage of falling snow events, the science is relatively new and retrievals are still undergoing development with challenges remaining. This work reports on the development and testing of retrieval algorithms for the Global Precipitation Measurement (GPM) mission Core Satellite, launched February 2014, with a specific focus on meeting GPM Mission requirements for falling snow

Learning from the 2018 heatwave in the context of climate change: Are high-temperature extremes important for adaptation in Scotland?

To understand whether high temperatures and temperature extremes are important for climate change adaptation in Scotland, we place the 2018 heatwave in the context of past, present, and future climate, and provide a rapid but comprehensive impact analysis. The observed hottest day, 5-day, and 30-day period of 2018 and the 5-day period with the warmest nights had return periods of 5-15 years for 1950-2018. The warmest night and the maximum 30-day average nighttime temperature were more unusual with return periods of >30 years. Anthropogenic climate change since 1850 has made all these high-temperature extremes more likely. Higher risk ratios are found for experiments from the CMIP6-generation global climate model HadGEM3-GA6 compared to those from the very-large ensemble system weather@home. Between them, the best estimates of the risk ratios for daytime extremes range between 1.2-2.4, 1.2-2.3, and 1.4-4.0 for the 1-, 5-, and 30-day averages. For the corresponding nighttime extremes, the values are higher and the ranges wider (1.5->50, 1.5-5.5, and 1.6->50). The short-period nighttime extremes were more likely in 2018 than in 2017, suggesting a contribution from year-to-year climate variability to the risk enhancement of extreme temperatures due to anthropogenic effects. Climate projections suggest further substantial increases in the likelihood of 2018 temperatures between now and 2050, and that towards the end of the century every summer might be as hot as 2018. Major negative impacts occurred, especially on rural sectors, while transport and water infrastructure alleviated most impacts by implementing costly special measures. Overall, Scotland could cope with the impacts of the 2018 heatwave. However, given the likelihood increase of high-temperature extremes, uncertainty about consequences of even higher temperatures and/or repeated heatwaves, and substantial costs of preventing negative impacts, we conclude that despite its cool climate, high-temperature extremes are important to consider for climate change adaptation in Scotland

FGFR3 and TP53 mutation analysis in inverted urothelial papilloma: incidence and etiological considerations

n/

Event attribution of Parnaíba River floods in Northeastern Brazil

The climate modeling techniques of event attribution enable systematic assessments of the extent that anthropogenic climate change may be altering the probability or magnitude of extreme events. In the consecutive years of 2018, 2019, and 2020, rainfalls caused repeated flooding impacts in the lower Parnaíba River in Northeastern Brazil. We studied the effect that alterations in precipitation resulting from human influences on the climate had on the likelihood of flooding using two ensembles of the HadGEM3-GA6 atmospheric model: one driven by both natural and anthropogenic forcings; and the other driven only by natural atmospheric forcings, with anthropogenic changes removed from sea surface temperatures and sea ice patterns. We performed hydrological modeling to base our assessments on the peak annual streamflow. The change in the likelihood of flooding was expressed in terms of the ratio between probabilities of threshold exceedance estimated for each model ensemble. With uncertainty estimates at the 90% confidence level, the median (5% 95%) probability ratio at the threshold for flooding impacts in the historical period (1982–2013) was 1.12 (0.97 1.26), pointing to a marginal contribution of anthropogenic emissions by about 12%. For the 2018, 2019, and 2020 events, the median (5% 95%) probability ratios at the threshold for flooding impacts were higher at 1.25 (1.07 1.46), 1.27 (1.12 1.445), and 1.37 (1.19 1.59), respectively; indicating that precipitation change driven by anthropogenic emissions has contributed to the increase of likelihood of these events by about 30%. However, there are other intricate hydrometeorological and anthropogenic processes undergoing long-term changes that affect the flood hazard in the lower Parnaíba River. Trend and flood frequency analyses performed on observations showed a nonsignificant long-term reduction of annual peak flow, likely due to decreasing precipitation from natural climate variability and increasing evapotranspiration and flow regulation

A whole-genome assay identifies four principal gene functions that confer tolerance of meropenem stress upon Escherichia coli

We report here the identification of four gene functions of principal importance for the tolerance of meropenem stress in Escherichia coli: cell division, cell envelope synthesis and maintenance, ATP metabolism, and transcription regulation. The primary mechanism of β-lactam antibiotics such as meropenem is inhibition of penicillin binding proteins, thus interfering with peptidoglycan crosslinking, weakening the cell envelope, and promoting cell lysis. However, recent systems biology approaches have revealed numerous downstream effects that are triggered by cell envelope damage and involve diverse cell processes. Subpopulations of persister cells can also arise, which can survive elevated concentrations of meropenem despite the absence of a specific resistance factor. We used Transposon-Directed Insertion Sequencing with inducible gene expression to simultaneously assay the effects of upregulation, downregulation, and disruption of every gene in a model E. coli strain on survival of exposure to four concentrations of meropenem. Automated Gene Functional Classification and manual categorization highlighted the importance at all meropenem concentrations of genes involved in peptidoglycan remodeling during cell division, suggesting that cell division is the primary function affected by meropenem. Genes involved in cell envelope synthesis and maintenance, ATP metabolism, and transcriptional regulation were generally important at higher meropenem concentrations, suggesting that these three functions are therefore secondary or downstream targets. Our analysis revealed the importance of multiple two-component signal transduction mechanisms, suggesting an as-yet unexplored coordinated transcriptional response to meropenem stress. The inclusion of an inducible, transposon-encoded promoter allowed sensitive detection of genes involved in proton transport, ATP production and tRNA synthesis, for which modulation of expression affects survival in the presence of meropenem: a finding that would not be possible with other technologies. We were also able to suggest new targets for future antibiotic development or for synergistic effects between gene or protein inhibitors and existing antibiotics. Overall, in a single massively parallel assay we were able to recapitulate many of the findings from decades of research into β-lactam antibiotics, add to the list of genes known to be important for meropenem tolerance, and categorize the four principal gene functions involved