ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Do environmental conservation programs contribute to sustainable livelihoods? Evidence from China's grain-for-green program in northern Shaanxi province

Several studies reported that conservation programs worldwide have profoundly reshaped participants' livelihoods and influenced other socio-economic processes. A fully understanding of how such conservation programs influence human lives is, therefore, crucial for their success. There, however, is little robust evidence of the effect of China's grain-for-green program (GGP), the largest conservation program in the world, on participants' livelihoods. That is, we do not know whether the program fulfills its goal of, at the very least, doing no harm to the lives of participants while simultaneously enhancing their environmental perception. To help fill this gap, we used a sustainable livelihood approach and structural equation modeling, based on household survey data from China's northern Shaanxi province, to compare the livelihood components of participants and non-participants in the GGP. We then characterized the interactions and pathways between their livelihood components and environmental perception. We found that the GGP indeed does no harm to participants' lives. Although participants suffer from a small reduction in natural capital due to a sharp decrease in their landholdings, they have much more off-farm income, subsidies, and financial and social assets than non-participants. Respondents commonly held positive attitudes toward the program's environmental benefits, but they had weak perceptions of the social and direct economic benefits of the GGP. Respondents' environmental perceptions of the GGP were significantly influenced by the number of available laborers, their education and health levels, off-farm income, subsidies, and the accessibility of transportation. Therefore, further resources should be dedicated to improving education as well as rural health care and infrastructure in order to create more off-farm job opportunities for GGP participants. In addition, decision makers should carefully redesign supporting policies, such as payments for ecosystem services, to help poor participants rebuild their livelihoods. (C) 2020 Elsevier B.V. All rights reserved.
The InVEST Habitat Quality Model Associated with Land Use/Cover Changes: A Qualitative Case Study of the Winike Watershed in the Omo-Gibe Basin, Southwest Ethiopia

The contribution of biodiversity to the global economy, human survival, and welfare has been increasing significantly, but the anthropogenic pressure as a threat to the pristine habitat has followed. This study aims to identify habitat suitability, analyze the change in habitat quality from 1988 to 2018, and to investigate the correlation between impact factors and habitat quality. The InVEST habitat quality model was used to analyze the spatiotemporal change in habitat quality in individual land-use types in the Winike watershed. Remote sensing data were used to analyze the land use/land cover changes. Nine threat sources, their maximum distance of impact, mode of decay, and sensitivity to threats were also estimated for each land-use cover type. The analysis illustrates that habitat degradation in the watershed was continuously increasing over the last three decades (1988 to 2018). Each threat impact factor and habitat sensitivity have increased for the last 30 years. The most contributing factor of habitat degradation was the 25.41% agricultural expansion in 2018. Population density, land-use intensity, elevation, and slope were significantly correlated with the distribution of habitat quality. Habitat quality degradation in the watershed during the past three decades suggested that the conservation strategies applied in the watershed ecosystem were not effective. Therefore, this study helps decision makers, particularly regarding the lack of data on biodiversity. It further looks into the conflict between economic development and conservation of biodiversity.
Influence of hybrid SiO2 and CeO2 nanoparticles on diesel engine emission operated with distilled tire oil blend

The objective of this research is to reduce the emission and improve the performance of diesel engine when they operate with distilled tire pyrolysis oil blends (DTPO30). Utilization of tire oil on diesel engine has been an active research field for the last two decades. But, it is still challengeable due to their high emissions. To overcome this, the present study has been attempted to analyze the influence of hybrid silicon oxide (SiO2) and cerium oxide (CeO2) nanoparticles in tire oil blends. The SiO2 and CeO2 are considered in hybrid nature with proportions of 20 mg/L (sum 40 mg/L), 50 mg/L, and 70 mg/L each. The nanoparticles mixed fuel blends were prepared using an ultrasonication technique. The experiments were conducted with a water-cooled engine with a constant speed of 1500 rpm, a compression ratio of 17.5, and an injection timing of 23° BTDC. The FTIR spectra indicated that all the samples were identified with alkanes and alkenes like diesel. But, DTPO30 + 70 found with many peaks in bending mode, which indicates the changes in chemical functional groups. From the engine testing, the brake thermal efficiency was improved by about 2% and brake specific fuel consumption was reduced up to 0.03 kg/kW h with DTPO30 + 70 (SiO2 + CeO2) than DTPO30. The cylinder pressure and heat release rate were reduced with hybrid nanoparticles blended fuel than with that of individually mixed fuel. Also, the emissions such as NOx, CO, UBHC, and soot were reduced with hybrid nanoparticles, up to 4.8%, 13.5%, 27%, and 10% respectively. The hybrid nature of SiO2 + CeO2 has the potential to adhere to the soot particles around their surface which leads to reducing the duration of suspension in the atmosphere.
A hybrid intelligent system for designing optimal proportions of recycled aggregate concrete

The replacement of natural coarse aggregate (NCA) with recycled coarse aggregate (RCA) in concrete mixtures offers various advantages, including conservation of natural resources, reduction of CO2 emissions, and cost reduction. However, multiple related variables and objectives (e.g., mechanical, economic, and environmental objectives) need to be considered when optimizing mixtures of recycled aggregate concrete (RAC). This cannot be achieved through traditional laboratory- or statistics-based methods. This study proposes a hybrid intelligent system based on artificial intelligence (AI) and metaheuristic algorithms for designing optimal mixtures of RAC. To verify the proposed model, a data set containing 344 different RAC mixtures was collected from previous literature. A semi-supervised cotraining algorithm using two k-nearest neighbor (kNN) regressors with different distance metrics is developed to label the unlabeled data in the collected dataset. Different AI models are incorporated into the system for modeling the relationship between RAC strength and its influencing variables. A multi-objective optimization (MOO) model based on AI algorithms and on a multi-objective firefly algorithm is used to search for optimal mixtures of RAC. The results show that kNN-based semi-supervised cotraining can effectively exploit unlabeled data to improve the regression estimates. In the test set, A Random Forest and Backpropagation Neural Network achieve the best prediction accuracy for predicting, respectively, uniaxial compressive strength and splitting tensile strength of RAC, indicated by the highest correlation coefficients (0.9064 and 0.8387, respectively) and lowest root-mean-square errors (6.639 MPa and 0.5119 MPa, respectively). The Pareto fronts of the multi-objective mixture optimization problem are successfully obtained by the MOO model. The proposed system can also be used to optimize mixture proportions of other cementitious materials in civil engineering. (C) 2020 Elsevier Ltd. All rights reserved.
Implications of peroxisome proliferator-activated receptor gamma (PPARY) with the intersection of organophosphate flame retardants and diet-induced obesity in adult mice

Previously, organophosphate flame retardants (OPFRs) were demonstrated to dysregulate homeostatic parameters of energy regulation within an adult mouse model of diet-induced obesity. Using the same OPFR mixture consisting of 1 mg/kg/day of each triphenyl phosphate, tricresyl phosphate, and tris(1,3-dichloro-2-propyl)phosphate, the current study examined the role of peroxisome proliferator-activated receptor gamma (PPARγ) in OPFR-induced disruption by utilizing mice with brain-specific deletion of PPARγ (PPARγKO) fed either a low-fat diet (LFD) or high-fat diet (HFD). Body weight and composition, feeding behavior, glucose and insulin tolerance, circulating peptide hormones, and expression of hypothalamic genes associated with energy homeostasis were recorded. When fed HFD, the effects of OPFR on body weight and feeding behavior observed in the previous wild-type (WT) study were absent in mice lacking neuronal PPARγ. This posits PPARγ as an important target for eliciting OPFR disruption in a diet-induced obesity model. Interestingly, female PPARγKO mice, but not males, experienced many novel OPFR effects not noted in WT mice, including decreased fat mass, altered feeding behavior and efficiency, improved insulin sensitivity, elevated plasma ghrelin and hypothalamic expression of its receptor. Taken together, these data suggest both direct roles for PPARγ in OPFR disruption of obese mice and indirect sensitization of pathways alternative to PPARγ when neuronal expression is deleted.
From a non-targeted metabolomics approach to a targeted biomarkers strategy to highlight testosterone abuse in equine. Illustration of a methodological transfer between platforms and laboratories

In order to overcome the challenge associated with the screening of Anabolic-Androgenic Steroids abuses in animal competitions, a non-targeted liquid chromatography coupled to high resolution mass spectrometry based metabolomics approach was implemented on equine urine samples to highlight potential biomarkers associated with the administration of such compounds, using testosterone esters as model steroids. A statistical model relying on four potential biomarkers intensity could be defined to predict the status of the samples. With a routine application perspective, the monitoring of the highlighted potential biomarkers was first transferred into high-throughput liquid chromatography-selected reaction monitoring (LC-SRM). The model's performances and robustness of the approach were preserved and providing a first demonstration of metabolomics-based biomarkers integration within a targeted workflow using common benchtop MS instrumentation. In addition, with a view to the widespread implementation of such biomarker-based tools, we have transferred the method to a second laboratory with similar instrumentation. This proof of concept allows the development and application of biomarker-based strategies to meet current doping control needs.
Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion

Invasive Spartina alterniflora (S. alterniflora), a native riparian species in the U.S. Gulf of Mexico, has led to serious degradation to the ecosystem and biodiversity as well as economic losses since it was introduced to China in 1979. Although multi-temporal remote sensing offers unique capability to monitor S. alterniflora over large areas and long time periods, three major hurdle exist: (1) in the coastal zone where S. alterniflora occupies, frequent cloud coverage reduces the number of available images that can be used; (2) prominent spectral variations exist within the S. alterniflora due to phonological variations; (3) poor spectral separability between S. alterniflora and its co-dominant native species is often presented in the territories where S. alterniflora intruded in. To articulate these questions, we proposed a new pixel-based phenological feature composite method (PpfCM) based on Google Earth Engine. The Ppf-CM method was brainstormed to battle the aforementioned three hurdles as the basic unit for extracting phonological feature is individual pixel in lieu of an entire image scene. With the Ppf-CM-derived phenological feature as inputs, we took a step further to investigate the performance of the latest deep learning method as opposed to that of the conventional support vector machine (SVM); Lastly, we strive to understand how S. alterniflora has changed its spatial distribution in the Beibu Gulf of China from 1995 to 2017. As a result, we found (1) the developed Ppf-CM method can mitigate the phonological variation and augment the spectral separability between S. alterniflora and the background species regardless of the significant cloud coverage in the study area; (2) deep learning, compared to SVM, presented better potentials for incorporating the new phenological features generated from the Ppf-CM method; and (3) for the first time, we discovered a S. alterniflora invasion outbreak occurred during 1996-2001.
Middle East respiratory syndrome coronavirus infection in camelids

Middle East respiratory syndrome coronavirus (MERS-CoV) is the cause of a severe respiratory disease with a high case fatality rate in humans. Since its emergence in mid-2012, 2578 laboratory-confirmed cases in 27 countries have been reported by the World Health Organization, leading to 888 known deaths due to the disease and related complications. Dromedary camels are considered the major reservoir host for this virus leading to zoonotic infection in humans. Dromedary camels, llamas, and alpacas are susceptible to MERS-CoV, developing a mild-to-moderate upper respiratory tract infection characterized by epithelial hyperplasia as well as infiltration of neutrophils, lymphocytes, and some macrophages within epithelium, lamina propria, in association with abundant viral antigen. The very mild lesions in the lower respiratory tract of these camelids correlate with absence of overt illness following MERS-CoV infection. Unfortunately, there is no approved antiviral treatment or vaccine for MERS-CoV infection in humans. Thus, there is an urgent need to develop intervention strategies in camelids, such as vaccination, to minimize virus spillover to humans. Therefore, the development of camelid models of MERS-CoV infection is key not only to assess vaccine prototypes but also to understand the biologic mechanisms by which the infection can be naturally controlled in these reservoir species. This review summarizes information on virus-induced pathological changes, pathogenesis, viral epidemiology, and control strategies in camelids, as the intermediate hosts and primary source of MERS-CoV infection in humans.
Surgical Techniques for Thoracolumbar Spine Fractures: WFNS Spine Committee Recommendations

To formulate the specific guidelines for the recommendation of thoracolumbar fracture regarding surgical techniques and nonfusion surgery. WFNS (World Federation of Neurosurgical Societies) Spine Committee organized 2 consensus meeting. For nonfusion surgery and thoracolumbar fracture, a systematic literature search in PubMed and Google Scholar database was done from 2010 to 2020. The search was further refined by excluding the articles which were duplicate, not in English or were based on animal or cadaveric subjects. After thorough shortlisting, only 50 articles were selected for full review in this consensus meeting. To generate a consensus, the levels of agreement or disagreement on each item were voted independently in a blind fashion through a Likert-type scale from 1 to 5. The consensus was achieved when the sum for disagreement or agreement was ≥ 66%. Each consensus point was clearly defined with evidence strength, recommendation grade, and consensus level provided. A magnitude of prospective papers were analyzed to formulate consensus on various surgical techniques that can be employed to address different types of thoracolumbar fractures. Surgical treatment of thoracolumbar fractures can be a better option over the nonoperative approach, especially for those who cannot tolerate months in an orthosis or cast, such as those with multiple extremity injuries, skin lesions, obesity, and so forth. It generally allows early mobilization, less hospital stay, reduced pulmonary complications, and better correction of sagittal balance. Current available literature fails to demonstrate any statistically significant benefit of fusion surgery over nonfusion in thoracolumbar fractures.
The relationship between biomass energy consumption and human development: Empirical evidence from BRICS countries

The impact of biomass energy utilization is still a controversial issue, and there is no consensus among researchers. Previous researches paid attention to the effects of biomass energy consumption on economic growth and environmental. While most studies indicate that the usage of biomass energy enhances economic growth and contributes to environmental protection, some studies show the opposite result. Our research wishes to contribute to the existing literature by discovering the effect of biomass energy consumption on human development in BRICS countries in the period 1990-2015. Using econometric methods which can solve the problem of cross-sectional dependence and heterogeneity of slope such as LIPS and CADF unit root tests, LM bootstrap panel cointegration test, Continuously-Updated Fully-Modified (CUP-FM) and Continuously-Updated Bias-Corrected (CUP-BC) estimators, and Dumitrescu-Hurlin panel causality test, our results reveal that biomass energy usage increase human development in BRICS countries and bidirectional causality exists between these two variables. These results may be a suggestion for policymakers to promote the usage of biomass energy. (C) 2020 Elsevier Ltd. All rights reserved.
Seasonal Betacoronavirus Antibodies' Expansion Post-BNT161b2 Vaccination Associates with Reduced SARS-CoV-2 VoC Neutralization

SARS-CoV-2 vaccination is known to induce antibodies that recognize also variants of concerns (VoCs) of the virus. However, epidemiological and laboratory evidences indicate that these antibodies have a reduced neutralization ability against VoCs. We studied binding and neutralizing antibodies against the Spike protein domains and subunits of the Wuhan-Hu-1 virus and its alpha, beta, delta VoCs and of seasonal betacoronaviruses (HKU1 and OC43) in a cohort of 31 health care workers prospectively followed post-vaccination with BNT162b2-Comirnaty. The study of sequential samples collected up to 64 days post-vaccination showed that serological assays measuring IgG against Wuhan-Hu-1 antigens were a poor proxy for VoC neutralization. In addition, in subjects who had asymptomatic or mild COVID-19 prior to vaccination, the loss of nAbs following disease could be rapid and accompanied by post-vaccination antibody levels similar to those of naïve vaccinees. Interestingly, in health care workers naïve for SARS-CoV-2 infection, vaccination induced a rapid and transient reactivation of pre-existing seasonal coronaviruses IgG responses that was associated with a subsequent reduced ability to neutralize alpha and beta VoCs.
Does financial development and foreign direct investment improve environmental quality? Evidence from belt and road countries

This study examines the effect of financial development (FD) and foreign direct investment (FDI) on the environmental quality for the panel of 90 belt and road countries from 1990 to 2017. This study advances the knowledge of financial development by using the new comprehensive index, which is based on access, depth, and efficiency of financial markets and financial institutions and incorporated foreign direct investment as an important determinant of environmental quality. By applying the Driscoll-Kraay standard error pooled ordinary least square method, the empirical findings reveal that FD deteriorates the environmental quality by increasing the CO2 emissions, while FDI improves environmental quality and the relationship between economic growth (EG) and CO2 emissions is inverted U-shaped, i.e., presence of EKC hypothesis. The energy consumption and urbanization pollute the environment, while trade openness enhances the quality of the environment. Furthermore, the Dumitrescu-Hurlin (DH) panel causality test result confirms that the bidirectional causality exists among FD, trade openness, energy consumption, and urbanization with CO2 emissions. The empirical results provide new insights for policymakers and also have several implications for the betterment of environmental quality.
Large-scale production and transport of hydrogen from Norway to Europe and Japan: Value chain analysis and comparison of liquid hydrogen and ammonia as energy carriers

Low-carbon hydrogen is considered as one of the key measures to decarbonise continental Europe and Japan. Northern Norway has abundant renewable energy and natural gas resources which can be converted to low-carbon hydrogen. However, Norway is located relatively far away from these markets and finding efficient ways to transport this hydrogen to the end-user is critical. In this study, liquefied hydrogen (LH2) and ammonia (NH3), as H-2-based energy carriers, are analysed and compared with respect to energy efficiency, CO2 footprint and cost. It is shown that the LH2 chain is more energy efficient and has a smaller CO2 footprint (20 and 23 kg-CO2/MWh(th) for Europe and Japan, respectively) than the NH3 chain (76 and 122 kg-CO2/MWh(thY)). Furthermore, the study finds the levelized cost of hydrogen delivered to Rotterdam to be lower for LH2 (5.0 EUR/kg-H-2) compared to NH3 (5.9 EUR/kg-H-2), while the hydrogen costs of the two chains for transport to Japan are in a similar range (about 7 EUR/kg-H-2). It is also shown that under optimistic assumptions, the costs associated with the LH2 chain (3.2 EUR/kg-H-2) are close to meeting the 2030 hydrogen cost target of Japan (2.5 EUR/kg-H-2). (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Enhancing waste to hydrogen production through biomass feedstock blending: A techno-economic-environmental evaluation

Concerns related to global warming and the depletion of fossil fuels have propelled the global community to explore alternative renewable energy sources. Biomass is a nonconventional and renewable energy resource that can potentially be utilised for the production of sustainable heat and power. The thermal gasification process is an effective biomass conversion and utilisation method producing syngas as the product gas. This study details a biomass gasification process and subsequent optimisation, considering multiple parameters, including the type of feedstock and gasifying agent (gasifier) to maximise hydrogen production. Aspen Plus software is used to develop three agent-based biomass gasification models considering the characteristics of certain materials from Qatar built environment. The ultimate goal of the study is to optimise the gasification processes to yield different biomass blending options satisfying the maximisation of hydrogen generation through different scenarios. The capabilities of the built-in activated analysis package using Aspen Energy Analyser and Aspen Process Economic Analyser are utilised to evaluate the environmental and economic perspectives. The results demonstrate the excellence of steam-only biomass gasification in providing profitable and cleaner products. The yield of hydrogen production from blending of biomass feedstock achieved a high fraction of 5.23% with the steam-only gasification, while the yield increased from 1.63% to 5.22% for the oxygen/steam gasification when maximising the hydrogen fraction. Moreover, the selective limiting of biomass capacity enhances the quality of syngas through enriching the hydrogen production and lowers the need for subsequent adjustment and the manipulation of gasifying agent quantity and operating energy.