Assessing the role of green investments and green innovation in ecological sustainability: From a climate action perspective on European countries

In recent years, economies have been increasingly focused on achieving the United Nations' Sustainable Development Goals, recognizing that their achievement is vital to ecological sustainability and green growth. In this context, this paper focuses on investigating the impact of green innovation, green investment, economic growth, and natural resources on ecological sustainability in the five best-performing European Union countries in terms of the Climate Change Performance Index. This study uses the load capacity factor as a comprehensive proxy of ecological sustainability and also assesses the load capacity curve hypothesis in sample nations. Continuously updated fully modified and continuously updated bias-corrected estimators are used to analyze the data from 1995 to 2020 in the context of climate action perspective. The econometric analysis revealed that the load capacity curve hypothesis is invalid in the sample countries. Natural resources decrease environmental sustainability. However, green investments and green innovations contribute to environmental quality and thereby, can be used for effective climate action. Based on these findings, the study recommends specific policies to achieve the Sustainable Development Goals, with a particular focus on target 13.

Identification of RNA‐dependent liquid‐liquid phase separation proteins using an artificial intelligence strategy

RNA-dependent liquid-liquid phase separation (LLPS) proteins play critical roles in cellular processes such as stress granule formation, DNA repair, RNA metabolism, germ cell development, and protein translation regulation. The abnormal behavior of these proteins is associated with various diseases, particularly neurodegenerative disorders like amyotrophic lateral sclerosis and frontotemporal dementia, making their identification crucial. However, conventional biochemistry-based methods for identifying these proteins are time-consuming and costly. Addressing this challenge, our study developed a robust computational model for their identification. We constructed a comprehensive dataset containing 137 RNA-dependent and 606 non-RNA-dependent LLPS protein sequences, which were then encoded using amino acid composition, composition of K-spaced amino acid pairs, Geary autocorrelation, and conjoined triad methods. Through a combination of correlation analysis, mutual information scoring, and incremental feature selection, we identified an optimal feature subset. This subset was used to train a random forest model, which achieved an accuracy of 90% when tested against an independent dataset. This study demonstrates the potential of computational methods as efficient alternatives for the identification of RNA-dependent LLPS proteins. To enhance the accessibility of the model, a user-centric web server has been established and can be accessed via the link: http://rpp.lin-group.cn.

Secondary attack rates and determinants of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) household transmission in Pakistan: A case-ascertained prospective, longitudinal study

BACKGROUND

Households are considered ideal settings for studying the transmission dynamics of an infectious disease.

METHODS

A prospective study was conducted, based on the World Health Organization FFX protocol from October 2020 to January,2021. Household contacts of laboratory-confirmed index cases were followed up for their symptomatic history, nasal swabs for RT-PCR,and blood samples for anti-SARS CoV-2 antibodies were collected at enrollment and days 7, 14 and 28. We estimated secondary attack rate (SAR), effective household case cluster size and determinants of secondary infection among susceptible household contacts using multivariable logistic regression.

RESULTS

We enrolled 77 index cases and their 543 contacts. Out of these, 252 contacts were susceptible at the time of enrollment. There were 77 household clusters, out of which, transmission took place in 20 (25.9%) giving rise to 34 cases. The acquired secondary attack rate (SAR) was 14.0% (95% CI 9.0-18.0). The effective household case cluster size was 0.46 (95%CI 0.33,0.56). Reported symptoms of nausea and vomiting (aOR, 7.9; 95% CI, 1.4-45.5) and fatigue (aOR, 9.3; 95% CI, 3.8-22.7) were associated with SARS-CoV-2 transmission.

CONCLUSIONS

We observed a low SARS-CoV-2 secondary attack rate in the backdrop of high seroprevalence and asymptomatic transmission among households in Karachi, Pakistan.

Contemporary Views of the Extraction, Health Benefits, and Industrial Integration of Rice Bran Oil: A Prominent Ingredient for Holistic Human Health

Globally, 50% of people consume rice (Oryza sativa), which is among the most abundant and extensively ingested cereal grains. Rice bran is a by-product of the cereal industry and is also considered a beneficial waste product of the rice processing industry. Rice bran oil (RBO) is created from rice bran (20-25 wt% in rice bran), which is the outermost layer of the rice kernel; has a lipid content of up to 25%; and is a considerable source of a plethora of bioactive components. The main components of RBO include high levels of fiber and phytochemicals, including vitamins, oryzanols, fatty acids, and phenolic compounds, which are beneficial to human health and well-being. This article summarizes the stabilization and extraction processes of rice bran oil from rice bran using different techniques (including solvent extraction, microwaving, ohmic heating, supercritical fluid extraction, and ultrasonication). Some studies have elaborated the various biological activities linked with RBO, such as antioxidant, anti-platelet, analgesic, anti-inflammatory, anti-thrombotic, anti-mutagenic, aphrodisiac, anti-depressant, anti-emetic, fibrinolytic, and cytotoxic activities. Due to the broad spectrum of biological activities and economic benefits of RBO, the current review article focuses on the extraction process of RBO, its bioactive components, and the potential health benefits of RBO. Furthermore, the limitations of existing studies are highlighted, and suggestions are provided for future applications of RBO as a functional food ingredient.

Fostering innovation and sustainable thinking in surgery: an evaluation of a surgical hackathon

INTRODUCTION

Surgery represents a major source of carbon emissions, with numerous initiatives promoting more sustainable practices. Healthcare innovation and the development of a digitally capable workforce are fundamental in leveraging technologies to tackle challenges, including sustainability in surgery.

METHODS

A surgical hackathon was organised with three major themes: (1) how to make surgery greener, (2) the future of plastic surgery in 10 years, and (3) improving healthcare outcomes using machine learning. Lectures were given on sustainability and innovation using the problem, innovation, market size, strategy and team (PIMST) framework to support their presentations, as well as technological support to translate ideas into simulations or minimum viable products. Pre- and post-event questionnaires were circulated to participants.

RESULTS

Most attendees were medical students (65%), although doctors and engineers were also present. There was a significant increase in delegates' confidence in approaching innovation in surgery (+20%, p < 0.001). Reducing waste packaging (70%), promoting recyclable material usage (56%) and the social media dimension of public perceptions towards plastic surgery (40%) were reported as the most important issues arising from the hackathon. The top three prizes went to initiatives promoting an artificial intelligence-enhanced operative pathway, instrument sterilisation and an educational platform to teach students research and innovation skills.

CONCLUSIONS

Surgical hackathons can result in significant improvements in confidence in approaching innovation, as well as raising awareness of important healthcare challenges. Future innovation events may build on this to continue to empower the future workforce to leverage technologies to tackle healthcare challenges such as sustainability.

A Systematic Review of Perinuclear Antineutrophil Cytoplasmic Antibody-Associated Glomerulonephritis Following Coronavirus Disease 2019 Vaccination: A 2024 Update

Antineutrophil cytoplasmic antibody (ANCA)-associated glomerulonephritis (GN) is an immune-mediated kidney disease characterized by the inflammation of small blood vessels in the kidney, leading to renal impairment and potentially irreversible damage. Concerns have been raised over the reports of myeloperoxidase/perinuclear (MPO/p) ANCA GN following the coronavirus disease 2019 (COVID-19) vaccination. Our study provides a comprehensive insight into perinuclear anti-neutrophil cytoplasmic antibodies (p-ANCA) GN after COVID-19 vaccination. We conducted a comprehensive literature search on PubMed, Cochrane Library, and EMBASE using the Medical Subject Headings (MeSH) terms related to "covid-19 vaccine," "glomerulonephritis," "p-ANCA," and "MPO-ANCA" up to March 5, 2024, to include cases of p-ANCA-associated GN following COVID-19 vaccination. Of the 4,102 articles, we included 29, reporting 35 patients demonstrating COVID-19 vaccine-induced p-ANCA GN, with 23 (65.7%) females and a median age of 69 years (mean ± SD = 63.22 ± 16). Twenty-six (74.28%) patients received the mRNA vaccine (Pfizer = 19, Moderna = 7). Seventeen (48.57%) patients presented with p-ANCA GN after the second dose of the COVID-19 vaccine, with a median gap of 19 days (1-84 days). Constitutional symptoms (54.28%) and acute kidney injury (42.85%) were the most reported initial presentations, and elevated serum creatinine (mean peak serum creatinine = 4.98 ± 5.02 mg/dL), hematuria, and proteinuria were the laboratory findings. MPO/p-ANCA was positive in 31 (88.6%) patients. All patients underwent renal biopsy, and crescentic GN was the most common finding among 27 (77.14%) patients. Management of p-ANCA GN included steroids in 30 (85.71%) patients, followed by rituximab (28.57%), and plasmapheresis (22.86%). Most patients responded well to treatment, with complete remission in 29 (82.86%) and relapse in four (11.42%) patients. Two patients did not achieve remission and became dialysis dependent. ANCA-associated GN is a rare and life-threatening complication of the COVID-19 vaccine, necessitating urgent evaluation and management. COVID-19 vaccine-induced p-ANCA GN should be included in the differential diagnoses of patients presenting with kidney injury after vaccination.

First Measurement Using Elliptically Polarized Photons of the Double-Polarization Observable E for γp→pπ^{0} and γp→nπ^{+}

We report the measurement of the helicity asymmetry E for the pπ^{0} and nπ^{+} final states using, for the first time, an elliptically polarized photon beam in combination with a longitudinally polarized target at the Crystal Ball experiment at MAMI. The results agree very well with data that were taken with a circularly polarized photon beam, showing that it is possible to simultaneously measure polarization observables that require linearly (e.g., G) and circularly polarized photons (e.g., E) and a longitudinally polarized target. The new data cover a photon energy range 270-1400 MeV for the pπ^{0} final state (230-842 MeV for the nπ^{+} final state) and the full range of pion polar angles, θ, providing the most precise measurement of the observable E. A moment analysis gives a clear observation of the pη cusp in the pπ^{0} final state.

Linking clean energy consumption, globalization, and financial development to the ecological footprint in a developing country: Insights from the novel dynamic ARDL simulation techniques

Developing countries have been facing economic difficulties for over three and a half decades due to numerous factors, including fossil fuel consumption and dwindling biocapacity. It is necessary to pinpoint the factors that may be culpable for poor environmental quality leading to a rising ecological footprint (EFP). This study explores the effect of clean energy, financial development (FDV), and globalization on the EFP in a developing country using the novel dynamic ARDL simulation techniques and the bootstrap causality test. The findings suggest that green energy has no meaningful impact on the EFP. Globalization and FDV significantly reduce the EFP by 0.25% and 0.08%, respectively. Besides, the findings confirm the existence of the EKC hypothesis. Furthermore, the causality results affirm a unidirectional causality from globalization and FDV to EFP, while economic growth drives globalization. Also, a one-way causality flows from globalization to FDV, just as FDV Granger causes green energy. In line with the findings, the study recommends that public policies focus on funding environmental-friendly technologies and green innovations. The funding must be on recently developed energy-saving technologies that can ensure complementarity between increased economic growth and environmental deterioration.

Drug-Induced Autoimmune Hepatitis: An Unusual Adverse Event of Atorvastatin Therapy

Drug-induced autoimmune hepatitis (AIH) is characterized by acute or chronic hepatic injury coupled with autoantibody development, hypertransaminasemia, and idiopathic AIH features on liver biopsy. Atorvastatin-induced AIH is a rare but well-documented life-threatening adverse event. We report a case of atorvastatin-induced AIH in a 57-year-old female who presented with worsening fatigue, jaundice, and deranged liver function tests. She had been prescribed atorvastatin 20 mg daily three months prior. Her clinical presentation, imaging findings, and serological testing were suggestive of drug-induced AIH. A liver biopsy confirmed a drug-induced autoimmune picture, and she was diagnosed with atorvastatin-induced AIH after ruling out all other possible causes. Her clinical presentation and liver enzymes improved after prolonged treatment with prednisone.

Assessing the interplay between political globalization, social globalization, democracy, militarization, and sustainable development: evidence from G-7 economies

Sustainable development can act as a catalyst in boosting environmental sustainability and human welfare by alleviating unsustainable production and consumption practices. Political globalization emerges as indispensable in increasing global environmental governance. In addition, social globalization, militarization, and democracy can also affect sustainable development. In light of the overlooked impacts of these crucial variables on sustainable development within prior research studies, this study investigates the heterogeneous effects of political globalization, militarization, social globalization, and democracy on sustainable development from 1990 to 2019 in the G-7 panel. The results obtained from the application of the methods of moment quantile regressions reveal that a one-percentage-point increase in political globalization yields a significant enhancement in sustainable development, ranging from 0.015 to 0.017% across the 10th to 90th quantiles. Contrarily, sustainable development exhibits a decline within the range of 0.025 to 0.028% across the 10th to 90th quantiles, on account of a 1% increase in social globalization. Likewise, militarization hampers sustainable development with a slightly increasing effect from the 10th to 90th quantiles. Gross fixed capital formation decreases sustainable development while the relationship between democracy and sustainable development indicates a negative correlation, which has not achieved statistical significance across the majority of quantiles. These novel outcomes are also verified by using some other regression tests. Subsequently, a detailed policy framework is presented for the purpose of fostering sustainable growth within the G-7 group.

Financial development, resource richness, eco-innovation, and sustainable development: Does geopolitical risk matter?

Financial development and geopolitical risks can significantly affect sustainable development. However, the roles of these factors in sustainable development are rarely investigated. Thus, this study takes into account the role of geopolitical risk while exploring the effects of financial development, natural resource rents, and eco-innovation on sustainable development in the Organization for Economic Co-operation and Development (OECD) countries. To this end, yearly data from 1990 to 2019 is analyzed using advanced econometric tests. The Common Correlated Effects Mean Group (CCEMG) results indicate that financial development and eco-innovation are significantly and positively related to sustainable development. Natural resource rents have a detrimental impact on sustainable development which confirms the presence of the resource curse hypothesis in OECD countries. Furthermore, the results revealed that controlling geopolitical risk is useful in fostering sustainable development. Lastly, the panel Granger causality test unveiled one-way causality from financial development, eco-innovation, natural resource rents, and geopolitical risk to sustainable development. Moreover, causalities are found from geopolitical risk to financial development, eco-innovation and natural resources. These findings suggest that OECD countries should prioritize financial development and eco-innovation policies for sustainable development while mitigating the negative effects of natural resource rents. The geopolitical risk can harm sustainable development, so policymakers should promote international cooperation and risk-sharing.

Distributed estimation and control over mobile sensor networks with jointly connected topology: Event-triggered approach

This article deals with the problem of distributed event-triggered tracking control in mobile sensor networks (MSNs) with a jointly connected topology (JCT). Two schemes are proposed for linear and Lipschitz nonlinear MSNs to estimate and track a mobile target. The proposed schemes are established using an event-triggered method to avoid continuous exchange of information between sensor nodes. In comparison with the other research under event-triggered communication strategies where states of the target are available, this paper considers that the states of the target are not available and two event-triggered algorithms are established for sensor nodes to estimate and follow the states of the continuous-time targets that can be seen in various real-world applications. Also, the proposed schemes are designed for the JCT with disconnected graphs which means the communication topology of the MSN is not required to be connected for all time instants. By employing the Cauchy convergence criterion and a common Lyapunov function, sufficient conditions are also established to ensure event-based tracking control subject to JCT. The effectiveness of the proposed work is verified by presenting simulation examples.

Multi-scale ultrasound induced composite coacervates of whey protein and pullulan polysaccharide on emulsion forming and stabilizing mechanisms

A non-destructive technique known as multi-scale ultrasound (MSU) was employed to modify the emulsion consisting of glycosylated bovine whey protein (WP) and pullulan (Pu). To assess the effect on the structural and emulsifying properties of the WP-Pu, the formulated emulsion, was treated with divergent MSU at (single: 20 kHz, 40 kHz, and 60 kHz; dual: 20-40 kHz, 40-60 kHz, and 20-60 kHz; and tri: 20-40-60 kHz) frequency for a duration of 30 min. The tri-frequency, treated emulsion showed improved emulsifying stability compared to the control and MSU-treated single, and dual-frequency samples, as indicated by the particle size, structural morphology, and adsorbed protein. The molecular docking and numerous spectral analysis provided evidence that WP can undergo successful phenolation. This modified form of WP then interacts with Pu through various forces, including H-bonding and other mechanisms, resulting in the formation of a composite emulsion. The rheological properties revealed that both the control emulsion and the MSU-treated emulsion exhibited non-Newtonian pseudoplastic flow behavior. This behavior is characterized by shear thinning, where the viscosity decreases with increasing shear rate. The shear rates tested ranged from 1 to 300 1/s, additionally, the degree of crystallinity increased from 18.2° to 19.4°. Overall, the tri-frequency effect was most pronounced compared to single and dual-frequency. Ultrasonication, an emerging non-thermal technology, proves to be an efficient approach for the formulation of WP-Pu composites. These composites have significant potential for use in drug delivery systems and functional foods.

Deep-STP: a deep learning-based approach to predict snake toxin proteins by using word embeddings

Snake venom contains many toxic proteins that can destroy the circulatory system or nervous system of prey. Studies have found that these snake venom proteins have the potential to treat cardiovascular and nervous system diseases. Therefore, the study of snake venom protein is conducive to the development of related drugs. The research technologies based on traditional biochemistry can accurately identify these proteins, but the experimental cost is high and the time is long. Artificial intelligence technology provides a new means and strategy for large-scale screening of snake venom proteins from the perspective of computing. In this paper, we developed a sequence-based computational method to recognize snake toxin proteins. Specially, we utilized three different feature descriptors, namely g-gap, natural vector and word 2 vector, to encode snake toxin protein sequences. The analysis of variance (ANOVA), gradient-boost decision tree algorithm (GBDT) combined with incremental feature selection (IFS) were used to optimize the features, and then the optimized features were input into the deep learning model for model training. The results show that our model can achieve a prediction performance with an accuracy of 82.00% in 10-fold cross-validation. The model is further verified on independent data, and the accuracy rate reaches to 81.14%, which demonstrated that our model has excellent prediction performance and robustness.

Alahmad Y, Jaleel Z, Ahmed Z, Kaassamali R, Hasani AA, Al-Thani H, Omar A. The predictive value of CTSI scoring system in non-operative management of patients with splenic blunt trauma: The experience of a level 1 trauma center

Background

The spleen is one of the most injured organs following blunt abdominal trauma. The management options can be either operative or non-operative management (NOM) with either conservative management or splenic artery embolization. The implementation of CT in emergency departments allowed the use of CT imaging as a primary screening tool in early decision-making. Consecutively, new splenic injury scoring systems, such as the CT severity index (CTSI) reported was established.

Aim

The main aim of this study is to evaluate the effect of the implementation of CTSI scoring system on the management decision and outcomes in patients with blunt splenic trauma over 8 years in a level 1 trauma center.

Methods

This is a retrospective study including all adult patients with primary splenic trauma, having NOM and admitted to our hospital between 2013 and 2021.

Results

The analyses were conducted on ninety-nine patients. The average sample age was 32.7 ± 12.3 years old. A total of (63/99) patients had splenic parenchyma injury without splenic vascular injury. There is a statistically significant association between CTSI grade 3 injury and the development of delayed splenic vascular injury (p < 0.05). There is an association between severity of initial CTSI score and the risk of NOM/clinical failure (p = 0.02).

Conclusion

Our findings suggest implementing such a system in a level 1 trauma center will further improve the outcome of treatment for splenic blunt trauma. However, CTSI grade 3 is considered an increased risk of NOM failure, and further investigations are necessary to standardize its management.

Achieving SDG-13 in the Era of Conflicts: The Roles of Economic Growth and Government Stability

Establishing effective climate control and reducing the ecological footprint (EF) are necessary for pursuing Sustainable Development Goals (SDGs), in particular Goal 13. In this context, it is required to enhance the understanding of various factors that can either decrease or enhance the EF. In the literature to date, limited studies on external conflicts (EX) have reported diverse results, and also the impacts of government stability (GS) on EF are less explored. This study explores the roles of external conflicts, economic growth, and government stability on EF in the context of SDG-13. The study also contributes to the literature by examining the environmental effects of government stability and external conflicts for the first time in Pakistan. This research uses time-series methodologies on data from Pakistan from 1984 to 2018 for exploring the long-run relations and causal dynamics. The results unfolded that external conflicts stimulate and Granger cause EF and therefore expand environmental deterioration. Thus, limiting conflicts is in the favor of Pakistan to achieve SDG-13. Surprisingly, government stability also poses harmful impacts on environmental quality by enhancing the EF, indicating that stable governments focus on improving economic conditions rather than environmental quality. Moreover, the study proves the validity of the environmental Kuznets curve. Policy suggestions are made to move forward in achieving SDG-13 and to evaluate the effectiveness of government environmental policies.

Modelling dried noodle quality: Contribution of starch and protein physicochemical properties of 32 wheat cultivars

30 mainstream wheat breeds from China and 2 from Australian were evaluated to analyze the correlation between grain quality traits, protein/starch properties and the comprehensive quality of fine dried noodles (FDN), with a multiple regression analysis conducted to establish predictive equations. Results showed FDN quality was both determined by the protein content and quality, as well as the starch properties, especially pasting characteristics. The balance between gluten strength and starch swelling characteristics was a key point to produce high quality FDN. Zhoumai32 and APW were found to be excellent cultivars for FDN production. Gluten content and index, SDS sedimentation value, dough extensibility, setback and peak viscosity could be served as indicators for specializing FDN flour. The established predictive equations could well explain over 60% of the variation in noodle color, cooking time, hardness, chewiness, and extensibility. These results were hoped to be a fundamental step towards developing the related standards or regulations for specializing FDN flour and rapid noodle quality prediction.

Assessing the multitargeted antidiabetic potential of three pomegranate peel-specific metabolites: An in silico and pharmacokinetics study

Diabetes is a chronic metabolic disorder that occurs due to impaired secretion of insulin, insulin resistance, or both. Recent studies show that the antidiabetic drugs used to control hyperglycemic levels are associated with undesirable adverse effects. Therefore, developing a safe and effective medicine with antidiabetic potential is needed. In this context, in silico studies are considered a rapid, effectual, and cost-effective method in drug discovery procedures. It is evident from the literature that plant-based natural components have shown promising outcomes in drug development to alleviate various diseases and hence have diversified the screening of potential antidiabetic agents. Purposely, in the present study, an in silico approach was performed on three Punica granatum peel metabolites (punicalin, punicalagin, and ellagic acid). All these three compounds were docked against nine protein targets involved in glucose metabolism (GFAT, PTP1β, PPAR-ᵞ, TKIR, RBP4, α-amylase, α-glucosidase, GCK, and AQP-2). These three pomegranate-specific compounds demonstrated significant interactions with GFAT, PTP1β, PPAR-ᵞ, TKIR, RBP4, α-amylase, α-glucosidase, GCK, and AQP-2 protein targets. Specifically, punicalin, punicalagin, and ellagic acid revealed significant binding scores (-9.2, -9.3, -8.1, -9.1, -8.5, -11.3, -9.2, -9.5, -10.1 kcal/mol; -10, -9.9, -8.5, -8.9, -10.4, -9.0, -10.2, -9.4, -9.0 kcal/mol; and -8.1, -8.0, -8.0, -6.8, -8.7, -7.8, -8.3, -8.1, -8.1 kcal/mol, respectively), with nine protein targets mentioned above. Hence, punicalin, punicalagin, and ellagic acid can be promising candidates in drug discovery to manage diabetes. Furthermore, in vivo and clinical trials must be conducted to validate the outcomes of the current study.

Do life expectancy and hydropower consumption affect ecological footprint? Evidence from novel augmented and dynamic ARDL approaches

Human activities threaten the future of the ecosystem by emitting pollution to the air, water, and soil. Considering the increasing ecological footprint (EF), the study focuses on investigating the role of life expectancy and hydropower consumption by controlling also income, trade openness, and globalization on the environment under the environmental Kuznets curve (EKC) hypothesis for Turkey during 1971-2018. In this context, the study performs recently developed augmented autoregressive distributed lag (AARDL) and dynamic ARDL (DARDL) methods. The results show that (i) life expectancy increases the environmental pressure; (ii) hydropower consumption has no effect on the EF; (iii) globalization and trade openness reduce the EF; (iv) the EKC hypothesis is valid, but the estimated turning point lies between USD 19,914 and USD 20,571, which is far from the sample period in Turkey. From the overall results, it can be concluded that Turkey cannot solve environmental problems with insufficient income levels, an increasing elderly population, and ineffective use of hydropower. Hence, Turkey should rely on income much more, use hydropower much more efficiently, and benefit from the spillover effect of technological innovations related to globalization and foreign trade to significantly reduce the EF.

Analyzing economic growth, eco-innovation, and ecological quality nexus in E-7 countries: accounting for non-linear impacts of urbanization by using a new measure of ecological quality

The rising urban population and accelerated resource consumption pose major environmental challenges. Given that around 75% of global resources are consumed in urban areas, understanding the true association between urbanization (UR) and ecological quality is necessary for better urban strategies. Alongside this, eco-innovation (ECO) can limit environmental pollution and thus, it is the cornerstone of environmental policies. On that note, this research discloses the nexus between eco-innovation, economic growth (EGR), and ecological quality by considering the non-linear effects of UR in the emerging seven (E-7) nations from 1992 to 2018. Unlike previous literature, the newly developed indicator of ecological quality (the load capacity factor (LCF)) is adopted to understand the connection between UR, eco-innovation, and ecological quality since this new comprehensive indicator is based on both biocapacity and ecological footprint (EF). In the methodology, the "Continuously Updated-Fully Modified" (CuP-FM) test is used since this method offers diverse benefits for panel data estimation by accounting for major panel data estimation issues including autocorrelation, endogeneity, heteroscedasticity, fractional integration, and cross-sectional dependence. The empirical estimates unveiled that the current levels as well as high levels of UR limit the LCF. This evidence suggests that UR in the E-7 nations expands ecological degradation. This finding contradicts the assertions of ecological modernization theory that expanding urbanization brings some ecological benefits. Similarly, economic growth alleviates ecological quality. However, eco-innovation and agricultural land (AGL) contribute to enhancing the LCF and promoting ecological quality. Further, UR and eco-innovation Granger cause the LCF. Lastly, comprehensive urban environmental strategies are discussed to promote sustainable urbanization backed by eco-innovation.

Evaluating the link between innovative human capital and regional sustainable development: Empirical evidence from China

Innovative human capital (IHC) can boost the economic growth of nations. However, in recent years, economies have become more attuned to sustainable development. In this context, it is important to assess the potential impact of IHC on green growth. Against this background, this study empirically examines the role of IHC on regional green growth in China, considering the spatial spillover effect and focusing on the quantity and quality of human capital and its direct and indirect effects on green growth. To this end, this paper adopts the spatial Durbin model, constructs an indicator system to evaluate green growth, and establishes a calculation formula for the quantity and quality of IHC. The empirical analysis provided some important findings. First, IHC and green growth have strong spatial correlation characteristics. Second, the quantity of IHC has a significant positive impact on regional green growth; however, the quality of IHC does not promote regional green growth. Third, the quantity and quality of IHC indirectly improve the level of regional green growth through technological progress. Finally, the role of IHC and its spatial spillover effect in improving the regional green growth level are most obvious in the central and western regions of China. Therefore, promoting green growth requires enhancing the accumulation of IHC and narrowing the gap between eastern and western China in the accumulation of IHC.

CFNCM: Collaborative filtering neighborhood-based model for predicting miRNA-disease associations

MicroRNAs have a significant role in the emergence of various human disorders. Consequently, it is essential to understand the existing interactions between miRNAs and diseases, as this will help scientists better study and comprehend the diseases' biological mechanisms. Findings can be employed as biomarkers or drug targets to advance the detection, diagnosis, and treatment of complex human disorders by foretelling possible disease-related miRNAs. This study proposed a computational model for predicting potential miRNA-disease associations called the Collaborative Filtering Neighborhood-based Classification Model (CFNCM), in light of the shortcomings of conventional and biological experiments, which are expensive and time-consuming. The model generated integrated miRNA and disease similarity matrices using the validated associations and miRNA and disease similarity information and used them as the input features for CFNCM. To produce class labels, we first determined the association scores for brand-new pairs using user-based collaborative filtering. With zero as the threshold, the associations with scores >0 were labelled 1, indicating a potential positive association, otherwise, it is marked as 0. Then, we developed classification models using various machine-learning algorithms. By comparison, we discovered that the support vector machine (SVM) produced the best AUC of 0.96 with 10-fold cross-validation through the GridSearchCV technique for identifying optimal parameter values. In addition, the models were evaluated and verified by analyzing the top 50 breast and lung neoplasms-related miRNAs, of which 46 and 47 associations were verified in two authoritative databases, dbDEMC and miR2Disease.

Green technologies, government stability, and green energy transition in a globalized world: evidence from E-7 nations

Unsustainable development and rising environmental degradation are major challenges for emerging nations that tend to promote human welfare by expanding economic development. Green energy transition (GETR) can help these nations to continue their development, reduce fossil fuel utilization, and achieve environmental sustainability. However, previous literature overlooks the importance of green technologies, government stability, and economic globalization in the GETR process. Accordingly, this research takes a step forward and assesses the impacts of green technologies (GT), government stability (GOV), and economic globalization (EGL) on green energy transition including population density (POP) and economic growth (GDP) in emerging seven (E-7) countries from 1992 to 2020. The research applied the "continuously updated fully modified (CuP-FM)" methodology to acquire the long-run findings robust to endogeneity stationary regressors, autocorrelation, and cross-sectional dependence (CD). The results highlighted that green technologies can be enhanced to accelerate the energy transition process since GETR and green technologies are positively connected. Also, government stability and economic globalization support the green energy transition. However, both population density and economic growth obstruct the energy transition process. The Emirmahmutoğlu and Kose test unveiled that green technologies, economic globalization, and government stability Granger cause the green energy transition. Based on these findings, policies are directed to promote the GETR by enhancing green technologies, economic globalization, and government stability for achieving ecological sustainability.