Exploring the facilitators and barriers to addressing social media's impact on anxiety within primary care: a qualitative study.

BACKGROUND: Several researchers and policymakers have acknowledged the alarming association between social media (SM) usage and anxiety symptoms in young adults. While primary care holds a crucial role in the improvement of health outcomes for those presenting with anxiety, there has been no research on GPs' perceptions of the impact of SM on anxiety. Furthermore, there has been little discussion of SM as a risk factor in anxiety-related consultations. This study is the first to use empirical research to inform how primary care can adapt to address SM's impact on anxiety within young adults. AIM: To identify the facilitators and barriers within primary care to addressing SM's impact on anxiety among young adults. DESIGN & SETTING: A qualitative study of GPs in the UK. METHOD: Following an exploratory pilot interview, semi-structured interviews with GPs (n = 7) were transcribed and thematically analysed, following an inductive approach. RESULTS: The following six facilitators were identified: a framework to facilitate discussion; open GP attitudes; GP training; referral pathways; larger stakeholder influence; and young adult education of social media's impact on anxiety. The following three barriers were identified: a lack of GP awareness of SM's impact on anxiety; cautious GP attitudes; and increased pressure on the health service. CONCLUSION: This qualitative study revealed a diversity of perceptions, and these novel findings are instructive in the adaptation of primary care services to meet the current mental health needs of young adults, as well as better assisting GPs in engaging in these conversations, especially within university practice.

Exploring the Impact of Social Media on Anxiety Among University Students in the United Kingdom: Qualitative Study.

BACKGROUND: The rapid surge in social media platforms has significant implications for users' mental health, particularly anxiety. In the case of social media, the impact on mental well-being has been highlighted by multiple stakeholders as a cause for concern. However, there has been limited research into how the association between social media and anxiety arises, specifically among university students-the generation that has seen the introduction and evolution of social media, and currently lives through the medium. Extant systematic literature reviews within this area of research have not yet focused on university students or anxiety, rather predominantly investigating adolescents or generalized mental health symptoms and disorders. Furthermore, there is little to no qualitative data exploring the association between social media and anxiety among university students. OBJECTIVE: The purpose of this study is to conduct a systematic literature review of the existing literature and a qualitative study that aims to develop foundational knowledge around the association of social media and anxiety among university students and enhance extant knowledge and theory. METHODS: A total of 29 semistructured interviews were conducted, comprising 19 male students (65.5%) and 10 female students (34.5%) with a mean age of 21.5 years. All students were undergraduates from 6 universities across the United Kingdom, with most students studying in London (89.7%). Participants were enrolled through a homogenous purposive sampling technique via social media channels, word of mouth, and university faculties. Recruitment was suspended at the point of data saturation. Participants were eligible for the study if they were university students in the United Kingdom and users of social media. RESULTS: Thematic analysis resulted in 8 second-order themes: 3 mediating factors that decrease anxiety levels and 5 factors that increase anxiety levels. Social media decreased anxiety through positive experiences, social connectivity, and escapism. Social media increased anxiety through stress, comparison, fear of missing out, negative experiences, and procrastination. CONCLUSIONS: This qualitative study sheds critical light on how university students perceive how social media affects their anxiety levels. Students revealed that social media did impact their anxiety levels and considered it an important factor in their mental health. Thus, it is essential to educate stakeholders, including students, university counselors, and health care professionals, about the potential impact of social media on students' anxiety levels. Since anxiety is a multifactorial condition, pinpointing the main stressors in a person's life, such as social media use, may help manage these patients more effectively. The current research highlights that there are also many benefits to social media, and uncovering these may help in producing more holistic management plans for anxiety, reflective of the students' social media usage.

Development of automated marine floating plastic detection system using Sentinel-2 imagery and machine learning models.

The increasing level of marine plastic pollution poses severe threats to the marine ecosystem and biodiversity. Open remote sensing data and advanced machine learning (ML) algorithms could be a cost-effective solution for identifying large plastic patches across the scale. The potential application of such resources in detecting and discriminating marine floating plastics (MFP) are not fully explored. Therefore, the present study attempted to explore the full functionality of open Sentinel satellite data and ML models for detecting and classifying the MFP in Mytilene (Greece), Limassol (Cyprus), Skala Loutron, Greece, Calabria (Italy), and Beirut (Lebanon). Two ML models, i.e. Support Vector Machine (SVM) and Random Forest (RF), were utilized to perform the classification analysis. In-situ plastic location data was collected from the control experiments conducted in Mytilene, Greece (in 2018 and 2019), Skala Loutron, Greece (2021), and Limassol, Cyprus (2018), and the same was considered for training the models. The accuracy and performances of the trained models were further tested on unseen new data collected from Calabria, Italy and Beirut, Lebanon. Both remote sensing bands and spectral indices were used for developing the ML models. A spectral signature profile for marine plastic was created for discriminating the floating plastic from other marine debris. A newly developed index, kernel Normalized Difference Vegetation Index (kNDVI), was incorporated into the modelling to examine its contribution to model performances. Both SVM and RF were performed well in five models and test case combinations. Among the two ML models, the highest performance was measured for the RF. The inclusion of kNDVI was found effective and increased the model performances, reflected by high balanced accuracy measured for model 2 (~89% to ~100% for SVM and ~92% to ~98% for RF). An automated floating plastic detection system was developed and tested in Calabria and Beirut using the best-performed model. The trained model had detected the floating plastic for both sites with ~80%-90%% accuracy. Among the six predictors, the Floating Debris Index (FDI) was the most important variable for detecting marine floating plastic. These findings collectively suggest that high-resolution remote sensing imagery and the automated ML models can be an effective alternative for the cost-effective detection of MFP. Future research will be directed toward collecting quality training data to develop robust automated models and prepare a spectral library for different plastic objects for discriminating plastic from other marine floating debris and advancing the marine plastic pollution research by taking full advantage of open-source data and technologies.

Daily angina documentation versus subsequent recall: development of a symptom smartphone app.

Aims: The traditional approach to documenting angina outcomes in clinical trials is to ask the patient to recall their symptoms at the end of a month. With the ubiquitous availability of smartphones and tablets, daily contemporaneous documentation might be possible. Methods and results: The ORBITA-2 symptom smartphone app was developed with a user-centred iterative design and testing cycle involving a focus group of previous ORBITA participants. The feasibility and acceptability were assessed in an internal pilot of participants in the ongoing ORBITA-2 trial. Seven days of app entries by ORBITA-2 participants were compared with subsequent participant recall at the end of the 7-day period. The design focus group tested a prototype app. They reported that the final version captured their symptoms and was easy to use. In the completion assessment group, 141 of 142 (99%) completed the app in full and 47 of 141 (33%) without reminders. In the recall assessment group, 29 of 29 (100%) participants said they could recall the previous day's symptoms, and 82% of them recalled correctly. For 2 days previously, 88% said they could recall and of those, 87% recalled correctly. The proportion saying they could recall their symptoms fell progressively thereafter: 89, 67, 61, 50%, and at 7 days, 55% (P < 0.001 for trend). The proportion of recalling correctly also fell progressively to 55% at 7 days (P = 0.04 for trend). Conclusion: Episode counts of angina are difficult to recall after a few days. For trials such as ORBITA-2 focusing on angina, daily symptom collection via a smartphone app will increase the validity of the results.

Nature-based solutions efficiency evaluation against natural hazards: Modelling methods, advantages and limitations.

Nature-based solutions (NBS) for hydro-meteorological risks (HMRs) reduction and management are becoming increasingly popular, but challenges such as the lack of well-recognised standard methodologies to evaluate their performance and upscale their implementation remain. We systematically evaluate the current state-of-the art on the models and tools that are utilised for the optimum allocation, design and efficiency evaluation of NBS for five HMRs (flooding, droughts, heatwaves, landslides, and storm surges and coastal erosion). We found that methods to assess the complex issue of NBS efficiency and cost-benefits analysis are still in the development stage and they have only been implemented through the methodologies developed for other purposes such as fluid dynamics models in micro and catchment scale contexts. Of the reviewed numerical models and tools MIKE-SHE, SWMM (for floods), ParFlow-TREES, ACRU, SIMGRO (for droughts), WRF, ENVI-met (for heatwaves), FUNWAVE-TVD, BROOK90 (for landslides), TELEMAC and ADCIRC (for storm surges) are more flexible to evaluate the performance and effectiveness of specific NBS such as wetlands, ponds, trees, parks, grass, green roof/walls, tree roots, vegetations, coral reefs, mangroves, sea grasses, oyster reefs, sea salt marshes, sandy beaches and dunes. We conclude that the models and tools that are capable of assessing the multiple benefits, particularly the performance and cost-effectiveness of NBS for HMR reduction and management are not readily available. Thus, our synthesis of modelling methods can facilitate their selection that can maximise opportunities and refute the current political hesitation of NBS deployment compared with grey solutions for HMR management but also for the provision of a wide range of social and economic co-benefits. However, there is still a need for bespoke modelling tools that can holistically assess the various components of NBS from an HMR reduction and management perspective. Such tools can facilitate impact assessment modelling under different NBS scenarios to build a solid evidence base for upscaling and replicating the implementation of NBS.

Examining the status of improved air quality in world cities due to COVID-19 led temporary reduction in anthropogenic emissions.

Clean air is a fundamental necessity for human health and well-being. Anthropogenic emissions that are harmful to human health have been reduced substantially under COVID-19 lockdown. Satellite remote sensing for air pollution assessments can be highly effective in public health research because of the possibility of estimating air pollution levels over large scales. In this study, we utilized both satellite and surface measurements to estimate air pollution levels in 20 cities across the world. Google Earth Engine (GEE) and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) application were used for both spatial and time-series assessment of tropospheric Nitrogen Dioxide (NO2) and Carbon Monoxide (CO) statuses during the study period (1 February to May 11, 2019 and the corresponding period in 2020). We also measured Population-Weighted Average Concentration (PWAC) of particulate matter (PM2.5 and PM10) and NO2 using gridded population data and in-situ air pollution estimates. We estimated the economic benefit of reduced anthropogenic emissions using two valuation approaches: (1) the median externality value coefficient approach, applied for satellite data, and (2) the public health burden approach, applied for in-situ data. Satellite data have shown that ~28 tons (sum of 20 cities) of NO2 and ~184 tons (sum of 20 cities) of CO have been reduced during the study period. PM2.5, PM10, and NO2 are reduced by ~37 (µg/m3), 62 (µg/m3), and 145 (µg/m3), respectively. A total of ~1310, ~401, and ~430 premature cause-specific deaths were estimated to be avoided with the reduction of NO2, PM2.5, and PM10. The total economic benefits (Billion US$) (sum of 20 cities) of the avoided mortality are measured as ~10, ~3.1, and ~3.3 for NO2, PM2.5, and PM10, respectively. In many cases, ground monitored data was found inadequate for detailed spatial assessment. This problem can be better addressed by incorporating satellite data into the evaluation if proper quality assurance is achieved, and the data processing burden can be alleviated or even removed. Both satellite and ground-based estimates suggest the positive effect of the limited human interference on the natural environments. Further research in this direction is needed to explore this synergistic association more explicitly.

Examining the effects of green revolution led agricultural expansion on net ecosystem service values in India using multiple valuation approaches.

Ecosystem Services (ESs) are bundles of natural processes and functions that are essential for human well-being, subsistence, and livelihoods. The 'Green Revolution' (GR) has substantial impact on the agricultural landscape and ESs in India. However, the effects of GR on ESs have not been adequately documented and analyzed. This leads to the main hypothesis of this work - 'the incremental trend of ESs in India is mainly prompted by GR led agricultural innovations that took place during 1960 - 1970'. The analysis was carried out through five successive steps. First, the spatiotemporal Ecosystem Service Values (ESVs) in Billion US$ for 1985, 1995, and 2005 were estimated using several value transfer approaches. Second, the sensitivity and elasticity of different ESs to land conversion were carried out using coefficient of sensitivity and coefficient of elasticity. Third, the Geographically Weighted Regression model was performed using five explanatory factors, i.e., total crop area, crop production, crop yield, net irrigated area, and cropping intensity, to explore the cumulative and individual effects of these driving factors on ESVs. Fourth, Multi-Layer Perceptron based Artificial Neural Network was employed to estimate the normalized importance of these explanatory factors. Fifth, simple and multiple linear regression modeling was done to assess the linear associations between the driving factors and the ESs. During the observation periods, cropland, forestland and water bodies contributed to 80%-90% of ESVs, followed by grassland, mangrove, wetland and urban built-up. In all three evaluation years, the highest estimated ESVs among the nine ES categories was provided by water regulation, followed by soil formation and soil-water retention, biodiversity maintenance, waste treatment, climate regulation, and greenhouse gas regulation. Among the five explanatory factors, total crop area, crop production, and net irrigated area showed strong positive associations with ESVs, while cropping intensity exhibited a negative association. Therefore, the study reveals a strong association between GR led agricultural expansion and ESVs in India. This study suggests that there should be an urgent need for formulation of rigorous ecosystem management strategies and policies to preserve ecological integrity and flow of uninterrupted ESs and to sustain human well-being.

Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach.

The socio-demographic factors have a substantial impact on the overall casualties caused by the Coronavirus (COVID-19). In this study, the global and local spatial association between the key socio-demographic variables and COVID-19 cases and deaths in the European regions were analyzed using the spatial regression models. A total of 31 European countries were selected for modelling and subsequent analysis. From the initial 28 socio-demographic variables, a total of 2 (for COVID-19 cases) and 3 (for COVID-19 deaths) key variables were filtered out for the regression modelling. The spatially explicit regression modelling and mapping were done using four spatial regression models such as Geographically Weighted Regression (GWR), Spatial Error Model (SEM), Spatial Lag Model (SLM), and Ordinary Least Square (OLS). Additionally, Partial Least Square (PLS) and Principal Component Regression (PCR) was performed to estimate the overall explanatory power of the regression models. For the COVID cases, the local R2 values, which suggesting the influences of the selected socio-demographic variables on COVID cases and death, were found highest in Germany, Austria, Slovenia, Switzerland, Italy. The moderate local R2 was observed for Luxembourg, Poland, Denmark, Croatia, Belgium, Slovakia. The lowest local R2 value for COVID-19 cases was accounted for Ireland, Portugal, United Kingdom, Spain, Cyprus, Romania. Among the 2 variables, the highest local R2 was calculated for income (R2 = 0.71), followed by poverty (R2 = 0.45). For the COVID deaths, the highest association was found in Italy, Croatia, Slovenia, Austria. The moderate association was documented for Hungary, Greece, Switzerland, Slovakia, and the lower association was found in the United Kingdom, Ireland, Netherlands, Cyprus. This suggests that the selected demographic and socio-economic components, including total population, poverty, income, are the key factors in regulating overall casualties of COVID-19 in the European region. In this study, the influence of the other controlling factors, such as environmental conditions, socio-ecological status, climatic extremity, etc. have not been considered. This could be the scope for future research.

Identification of Conservation Priority Zones Using Spatially Explicit Valued Ecosystem Services: A Case from the Indian Sundarbans.

Demarcation of conservation priority zones (CPZs) using spatially explicit models is the new challenge in ecosystem services (ESs) research. This study identifies the CPZs of the Indian Sundarbans by integrating 2 different approaches, that is, ESs and ecosystem health (EH). Five successive steps were followed to conduct the analysis: First, the ESs were estimated using biophysical and economic methods and a hybrid method (that combines biophysical and economic methods); second, the vigor-organization-resilience (VOR) model was used for estimating EH; third, the risk characterization value (RCV) of ESs was measured using the function of EH and ESs; fourth, Pearson correlation test was performed to analyze the interaction between ESs and EH components; and fifth, the CPZs were defined by considering 7 relevant components: ecosystem vigor, ecosystem organization, ecosystem resilience, RCV, EH, ESs, and the correlation between EH and ESs. Among the major ecoregions of the Sundarbans, the highest ESs value in economic terms is provided by the mangrove ecosystem (US$19 144.9 million per year). The highest conservation priority score was projected for the Gosaba block, which is dominated by dense mangrove forests. The estimated CPZs were found to be highly consistent with the existing biodiversity zonations. The outcome of this study could be a reference for environmentalists, land administrators, researchers, and decision makers to design relevant policies to protect the high values of the Sundarbans ecosystem. Integr Environ Assess Manag 2020;16:773-787. © 2020 SETAC.

Towards an operationalisation of nature-based solutions for natural hazards.

Nature-based solutions (NBS) are being promoted as adaptive measures against predicted increasing hydrometeorological hazards (HMHs), such as heatwaves and floods which have already caused significant loss of life and economic damage across the globe. However, the underpinning factors such as policy framework, end-users' interests and participation for NBS design and operationalisation are yet to be established. We discuss the operationalisation and implementation processes of NBS by means of a novel concept of Open-Air Laboratories (OAL) for its wider acceptance. The design and implementation of environmentally, economically, technically and socio-culturally sustainable NBS require inter- and transdisciplinary approaches which could be achieved by fostering co-creation processes by engaging stakeholders across various sectors and levels, inspiring more effective use of skills, diverse knowledge, manpower and resources, and connecting and harmonising the adaptation aims. The OAL serves as a benchmark for NBS upscaling, replication and exploitation in policy-making process through monitoring by field measurement, evaluation by key performance indicators and building solid evidence on their short- and long-term multiple benefits in different climatic, environmental and socio-economic conditions, thereby alleviating the challenges of political resistance, financial barriers and lack of knowledge. We conclude that holistic management of HMHs by effective use of NBS can be achieved with standard compliant data for replicating and monitoring NBS in OALs, knowledge about policy silos and interaction between research communities and end-users. Further research is needed for multi-risk analysis of HMHs and inclusion of NBS into policy frameworks, adaptable at local, regional and national scales leading to modification in the prevalent guidelines related to HMHs. The findings of this work can be used for developing synergies between current policy frameworks, scientific research and practical implementation of NBS in Europe and beyond for its wider acceptance.

Examining the effects of forest fire on terrestrial carbon emission and ecosystem production in India using remote sensing approaches.

Remote sensing techniques are effectively used for measuring the overall loss of terrestrial ecosystem productivity and biodiversity due to forest fires. The current research focuses on assessing the impacts of forest fires on terrestrial ecosystem productivity in India during 2003-2017. Spatiotemporal changes of satellite remote sensing derived burn indices were estimated for both fire and normal years to analyze the association between forest fires and ecosystem productivity. Two Light Use Efficiency (LUE) models were used to quantify the terrestrial Net Primary Productivity (NPP) of the forest ecosystem using the open-source and freely available remotely sensed data. A novel approach (delta NPP/delta burn indices) is developed to quantify the effects of forest fires on terrestrial carbon emission and ecosystem production. During 2003-2017, the forest fire intensity was found to be very high (>2000) across the eastern Himalayan hilly region, which is mostly covered by dense forest and thereby highly susceptible to wildfires. Scattered patches of intense forest fires were also detected in the lower Himalayan and central Indian states. The spatial correlation between the burn indices and NPP were mainly negative (-0.01 to -0.89) for the fire-prone states as compared to the other neighbouring regions. Additionally, the linear approximation between the burn indices and NPP showed a positive relation (0.01 to 0.63), suggesting a moderate to high impact of the forest fires on the ecosystem production and terrestrial carbon emission. The present approach has the potential to quantify the loss of ecosystem productivity due to forest fires.

A Possible Role of the Full-Length Nascent Protein in Post-Translational Ribosome Recycling.

Each cycle of translation initiation in bacterial cell requires free 50S and 30S ribosomal subunits originating from the post-translational dissociation of 70S ribosome from the previous cycle. Literature shows stable dissociation of 70S from model post-termination complexes by the concerted action of Ribosome Recycling Factor (RRF) and Elongation Factor G (EF-G) that interact with the rRNA bridge B2a/B2b joining 50S to 30S. In such experimental models, the role of full-length nascent protein was never considered seriously. We observed relatively slow release of full-length nascent protein from 50Sof post translation ribosome, and in that process, its toe prints on the rRNA in vivo and in in vitro translation with E.coli S30 extract. We reported earlier that a number of chemically unfolded proteins like bovine carbonic anhydrase (BCA), lactate dehydrogenase (LDH), malate dehydrogenase (MDH), lysozyme, ovalbumin etc., when added to free 70Sin lieu of the full length nascent proteins, also interact with identical RNA regions of the 23S rRNA. Interestingly the rRNA nucleotides that slow down release of the C-terminus of full-length unfolded protein were found in close proximity to the B2a/B2b bridge. It indicated a potentially important chemical reaction conserved throughout the evolution. Here we set out to probe that conserved role of unfolded protein conformation in splitting the free or post-termination 70S. How both the RRF-EFG dependent and the plausible nascent protein-EFG dependent ribosome recycling pathways might be relevant in bacteria is discussed here.

Mechanism of ribosome assisted protein folding: a new insight into rRNA functions.

The peptidyl transferase center (PTC), present in the domain V of 23S rRNA of bacteria can act as a general protein folding modulator. Any general function of a nucleic acid polymer (DNA or RNA) is always related to specific sequence/sequences. The ribosome mediated protein folding also involves a specific interaction between the nucleotides of peptidyl transferase center and the amino acids of an unfolded protein. In this article the mechanism of rRNA assisted protein folding and its significance in the light of high resolution crystal structure of ribosome are discussed.

Role of the ribosome in protein folding.

In all organisms, the ribosome synthesizes and folds full length polypeptide chains into active three-dimensional conformations. The nascent protein goes through two major interactions, first with the ribosome which synthesizes the polypeptide chain and holds it for a considerable length of time, and then with the chaperones. Some of the chaperones are found in solution as well as associated to the ribosome. A number of in vitro and in vivo experiments revealed that the nascent protein folds through specific interactions of some amino acids with the nucleotides in the peptidyl transferase center (PTC) in the large ribosomal subunit. The mechanism of this folding differs from self-folding. In this article, we highlight the folding of nascent proteins on the ribosome and the influence of chaperones etc. on protein folding.

Protein folding by domain V of Escherichia coli 23S rRNA: specificity of RNA-protein interactions.

The peptidyl transferase center, present in domain V of 23S rRNA of eubacteria and large rRNA of plants and animals, can act as a general protein folding modulator. Here we show that a few specific nucleotides in Escherichia coli domain V RNA bind to unfolded proteins and, as shown previously, bring the trapped proteins to a folding-competent state before releasing them. These nucleotides are the same for the proteins studied so far: bovine carbonic anhydrase, lactate dehydrogenase, malate dehydrogenase, and chicken egg white lysozyme. The amino acids that interact with these nucleotides are also found to be specific in the two cases tested: bovine carbonic anhydrase and lysozyme. They are either neutral or positively charged and are present in random coils on the surface of the crystal structure of both the proteins. In fact, two of these amino acid-nucleotide pairs are identical in the two cases. How these features might help the process of protein folding is discussed.

In vitro protein folding by E. coli ribosome: unfolded protein splitting 70S to interact with 50S subunit.

Folding of unfolded protein on Escherichia coli 70S ribosome is accompanied by rapid dissociation of the ribosome into 50S and 30S subunits. The dissociation rate of 70S ribosome with unfolded protein is much faster than that caused by combined effect of translation and polypeptide release factors known to be involved in the dissociation of ribosome into subunits. The protein then reaches a "folding competent" state on 50S and is released to take up native conformation by itself. Release before attaining the folding competent state or prevention of release by cross-linking it with ribosome, would not allow the protein to get back to its native conformation.

Protein folding following synthesis in vitro and in vivo: association of newly synthesized protein with 50S subunit of E. coli ribosome.

In the accompanying paper, it was shown that a protein, while reverting to native form from the unfolded state in vitro with the help of bacterial 70S ribosome, split the latter into its subunits (50S and 30S) and remains associated with the 50S subunit. Here, we follow the fate of nascent proteins both in case of in vivo and in vitro translation system. The newly synthesised protein was found to associate with the 50S subunit in both the cases.

Ribosome-DnaK interactions in relation to protein folding.

Bacterial ribosomes or their 50S subunit can refold many unfolded proteins. The folding activity resides in domain V of 23S RNA of the 50S subunit. Here we show that ribosomes can also refold a denatured chaperone, DnaK, in vitro, and the activity may apply in the folding of nascent DnaK polypeptides in vivo. The chaperone was unusual as the native protein associated with the 50S subunit stably with a 1:1 stoichiometry in vitro. The binding site of the native protein appears to be different from the domain V of 23S RNA, the region with which denatured proteins interact. The DnaK binding influenced the protein folding activity of domain V modestly. Conversely, denatured protein binding to domain V led to dissociation of the native chaperone from the 50S subunit. DnaK thus appears to depend on ribosomes for its own folding, and upon folding, can rebind to ribosome to modulate its general protein folding activity.