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Background Despite increased understanding of psoriasis pathogenesis, molecular classification of clinical phenotypes and disease severity is poorly defined. Knowledge gaps include whether molecular endotypes of psoriasis underlie distinct clinical phenotypes and the positive and negative molecular regulators of disease severity across tissue compartments. Methods We performed comprehensive RNA sequencing of skin and blood (n = 718) from prospectively-recruited, deeply-phenotyped discovery and replication cohorts of 146 subjects with moderate-to-severe chronic plaque psoriasis initiating TNF-inhibitor (adalimumab) or IL-12/23-inhibitor (ustekinumab) therapy. Results Here we show, using two complementary dimensionality reduction methods, that co-expressed gene modules and factors within skin and blood are significantly associated with psoriasis phenotypes and disease severity. We identify a 14-gene signature negatively associated with BMI in nonlesional skin and with disease severity in lesional skin. Genotype integration reveals that HLA-DQA1*01 and HLA-DRB1*15 genotypes are positively associated with baseline psoriasis severity. Using explainable machine learning models, we define two disease severity-associated gene modules in lesional skin - one positive, one negatively-associated - and a 9-gene signature in lesional skin predictive of disease severity. Disease severity signatures in blood are only seen following adalimumab exposure, suggesting greater systemic impact of adalimumab compared to ustekinumab, in line with its side effect profile. In contrast, a gene signature in blood linked to HLA-C*06:02 status is independent of disease severity or drug. Conclusions These findings delineate gene-environmental and genetic effects on the psoriasis transcriptome linked to disease severity. Plain language summary Psoriasis is a common and debilitating skin disease, linked to other inflammatory conditions. A lot is known about what causes psoriasis and the factors that influence it, but doctors still cannot offer personalised treatments. This is because it has been difficult to understand what makes psoriasis more or less severe, why people respond differently to treatment, or why some people develop related diseases. To help address this, we collected skin and blood samples and personal information from people with severe psoriasis across the United Kingdom. Using computer-based methods, we found shared biological processes that link the disease with obesity and help predict its severity. Rider, Grantham, Smith, Watson et al. integrate multiomic data from patients with psoriasis using dimensionality reduction and machine learning techniques. This approach identifies biological relationships between genetic background, clinical features and disease severity, providing insight into disease variability across individuals.

Our primary research questions are: (1) What is the association between whole grains (WG) intake and the prevalence of NCDs (i.e., type 2 diabetes, cardiovascular disease, obesity, cancer, mortality) and their biomarkers? (2) Which biomarker(s) has/have the greatest association with WG intake when combining multiple biomarkers together in the same analysis? Our secondary research question is: (3) Are there dose–response relationships between WG intake and biomarkers and prevalence of NCDs which could help inform a universal recommendation for WG intake?

The proposed protocol is for a systematic review and meta-analysis on the relationship between vitamin A and body mass. The primary objective is to explore the mechanisms between vitamin A and adiposity such as inflammation, dietary intake and body fat. The secondary objective is to look at the extent to which vitamin A is stored in different adipose tissue depots. The protocol outlines the motive and scope for the review, and methodology including the risk of bias, statistical analysis, screening and study criteria.

The proposed protocol is for a systematic review and meta-analysis on the effects of whole-grains (WG) on non-communicable diseases such as type 2 diabetes, cardiovascular disease, hypertension and obesity. The primary objectives is to explore the mechanisms of WG intake on multiple biomarkers of NCDs such as fasting glucose, fasting insulin and many others. The secondary objective will look at the dose-response relationship between these various mechanisms. The protocol outlines the motive and scope for the review, and methodology including the risk of bias, statistical analysis, screening and study criteria.

Despite increased understanding of psoriasis pathogenesis, molecular classification of clinical phenotypes and disease severity is poorly defined. Knowledge gaps include whether molecular endotypes of psoriasis underlie distinct clinical phenotypes and the positive and negative molecular regulators of disease severity across tissue compartments. We performed comprehensive RNA-sequencing of skin and blood (n=718) from prospectively-recruited, deeply-phenotyped discovery and replication cohorts of 146 subjects with moderate-to-severe psoriasis initiating TNF-inhibitor (adalimumab) or IL-12/23-inhibitor (ustekinumab) therapy. Using two complementary methods for dimensionality reduction, we defined distinct but interconnected co-expression modules and factors within skin and blood that were significantly associated with disease phenotypes and disease severity, as measured by Psoriasis Area Severity Index (PASI). We identified a 14-gene signature negatively associated with BMI in nonlesional skin and disease severity in lesional skin, respectively. Genotype integration revealed that HLA-DQA1*01 and HLA-DRB1*15 genotypes were positively associated with baseline disease severity. Using Gaussian process regression followed by SHAP (SHapley Additive exPlanations), we defined two core drug independent and disease severity-associated gene modules in lesional skin - one positive, one negative - and a lesional 9-gene signature predictive of disease severity. Disease severity signatures in blood were only seen following adalimumab exposure, suggesting greater systemic impact of adalimumab compared to ustekinumab, in line with its side effect profile. In contrast, a gene signature in blood linked to HLA-C*06:02 status was independent of disease severity or drug. These findings delineate gene-environmental and genetic effects on the psoriasis transcriptome linked to disease severity. Psoriasis is a common and debilitating skin disease, linked to multiple other inflammatory conditions. A lot is known about the mechanism of psoriasis and its inherited and external influences. Despite this, doctors cannot yet offer personalised treatments as it has been difficult to discover whether biological pathways are associated with disease severity, response to treatment or a person’s likelihood of having other linked diseases. To help address this, we collected skin and blood samples and the personal characteristics of a group of people with severe psoriasis across the United Kingdom. Using computer-based methods, we discovered common biological processes underlying different psoriasis types, including genes that connect psoriasis severity with obesity, and another set of genes that help predict disease severity.

People’s sentiments and perceptions of greenhouse gas emission and renewable energy are important information to understand their reaction to the planned mitigation policy. Therefore, this research analyzes people’s perceptions of greenhouse gas emissions and their preferences for renewable energy resources using a sample of Twitter data. We first identify themes of discussion using semantic text similarity and network analysis. Next, we measure people’s interest in renewable energy resources based on the mentioned rate in Twitter and search interest in Google trends. Then, we measure people’s sentiment toward these resources and compare the interest with sentiments to identify opportunities for policy improvement. The results indicate a minor influence of governmental assemblies on Twitter discourses compared to a very high influence of two renewable energy providers amounts to more than 40% of the tweeting activities related to renewable energy. The search interest analysis shows a slight shift in people’s interest in favor of renewable energy. The interest in geothermal energy is decreasing while interest in biomass energy is increasing. The sentiment analysis shows that biomass energy has the highest positive sentiments while solar and wind energy have higher interest. Solar and wind energy are found to be the two most promising sources for the future energy transition. Our study implies that governments should practice a higher influence on promoting awareness of the environment and converging between people’s interests and feasible energy solutions. We also advocate Twitter as a source for collecting real-time data about social preferences for environmental policy input.

During the COVID-19 outbreak, managing energy consumption and CO2 emission remained a serious problem. The previous literature rarely solved this real-time issue, and there is a lack of public research proposing an effective way forward on it. However, the study examines the impact of the COVID-19 outbreak on energy consumption and CO2 emission. The design of the study is quantitative, and the data is acquired from different online databases. The model of the study is inferred by using panel unit root test and ARDL test. The robustness of study findings was checked through panel quantile regression. The findings highlighted that the COVID-19 outbreak is negatively significant with energy consumption and CO2 emission. The study suggested revising the energy consumption patterns by developing and implementing the national action plan for energy consumption and environmental protection. The study also contributed in knowledge by suggesting the novel insight into CO2 emission and energy consumption patterns during COVID-19 pandemic and recommended to consider renewable energy transition methods as an opportunity for the society. For a more effective management of energy consumption and environmental pollution, country-specific measures are suggested to be taken, and the national government should support the concerned public departments, ministries and private organizations on it. To the best of our study, this is one of the pioneer studies studying this novel link and suggesting the way forward on recent topicality.During the COVID-19 outbreak, managing energy consumption and CO2 emission remained a serious problem. The previous literature rarely solved this real-time issue, and there is a lack of public research proposing an effective way forward on it. However, the study examines the impact of the COVID-19 outbreak on energy consumption and CO2 emission. The design of the study is quantitative, and the data is acquired from different online databases. The model of the study is inferred by using panel unit root test and ARDL test. The robustness of study findings was checked through panel quantile regression. The findings highlighted that the COVID-19 outbreak is negatively significant with energy consumption and CO2 emission. The study suggested revising the energy consumption patterns by developing and implementing the national action plan for energy consumption and environmental protection. The study also contributed in knowledge by suggesting the novel insight into CO2 emission and energy consumption patterns during COVID-19 pandemic and recommended to consider renewable energy transition methods as an opportunity for the society. For a more effective management of energy consumption and environmental pollution, country-specific measures are suggested to be taken, and the national government should support the concerned public departments, ministries and private organizations on it. To the best of our study, this is one of the pioneer studies studying this novel link and suggesting the way forward on recent topicality.

Environmental problems, including extreme weather phenomena, unprecedented global warming, and environmental disasters caused by increasing levels of CO 2 and other toxic emissions, along with rapidly increasing economic development and energy consumption, require global development and policies to meet sustainable development goals. The traditional data envelopment analysis (DEA) model has limited practical applicability for measuring environmental performance, as it lacks the computational capacity to deal with undesirable outputs. The current study employs “radial” and “non-radial” DEA technology, and acknowledges the associations of a mathematical foundation to increase the analytical capability of the environmental performance of DEA. Results show that in the measurement of environmental performance analysis, the non-radial DEA model has a higher discriminating power compared to radial DEA. Results show that the average values of radial and non-radial environmental performance are highest for Latin America and the Caribbean, at 0.99 and 0.96, respectively, while the former USSR has the lowest values of 0.22 and 0.32, respectively. The South Asian region shows relatively stable values of about 0.58 to 0.65, and Latin America & Caribbean countries and sub-Saharan Africa also show a stable radial environmental performance ranging from 0.82 to 1.00. These results indicate a considerable difference among the eight world regions.

Agricultural production is vital for the stability of the country’s economy. Controlling weed infestation through agrochemicals is necessary for increasing crop productivity. However, its excessive use has severe repercussions on the environment (damaging the ecosystem) and the human operators exposed to it. The use of Unmanned Aerial Vehicles (UAVs) has been proposed by several authors in the literature for performing the desired spraying and is considered safer and more precise than the conventional methods. Therefore, the study’s objective was to develop an accurate real-time recognition system of spraying areas for UAVs, which is of utmost importance for UAV-based sprayers. A two-step target recognition system was developed by using deep learning for the images collected from a UAV. Agriculture cropland of coriander was considered for building a classifier for recognizing spraying areas. The developed deep learning system achieved an average F1 score of 0.955, while the classifier recognition average computation time was 3.68 ms. The developed deep learning system can be deployed in real-time to UAV-based sprayers for accurate spraying.

In order to achieve the goal of sustainable green economic development, it is necessary to conduct a comprehensive assessment of the efficiency of the green economy and compare it with emission reductions. The green economy idea is a much-discussed solution to economic growth. Therefore, this study investigated the impact of government spending on the performance of the green economy of various countries under the \"Belt and Road\" (BRI) initiative project. The data were analyzed using the BRI economy panel data from 2008 to 2018. The generalized method of moments (GMM) was used to estimate the effect of government expenditures on education and research and development (R&D) on green economic performance index (GEE) in BRI countries. The results reveal that during the study period, BRI countries have experienced an upward transition towards green development, except for Pakistan and Bangladesh; their GEE decreased gradually from 2010 to 2018. Further, the findings of the system GMM revealed that both education and R&D have a positive impact on the green economy. Moreover, the compositional and technological effects of the overall sample were verified with the GMM process. Nevertheless, the sub-sample results revealed a heterogeneous impact on countries with a high per capita GDP. Following the results, useful policy measures for promoting sustainable green economic development have been proposed.