ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Modulation of soluble guanylate cyclase ameliorates pulmonary hypertension in a rat model of chronic thromboembolic pulmonary hypertension by stimulating angiogenesis

Acute pulmonary embolism (PE) does not always resolve after treatment and can progress to chronic thromboembolic disease (CTED) or the more severe chronic thromboembolic pulmonary hypertension (CTEPH). The mechanisms surrounding the likelihood of PE resolution or progress to CTED/CTEPH remain largely unknown. We have developed a rat model of CTEPH that closely resembles the human disease in terms of hemodynamics and cardiac manifestations. Embolization of rats with polystyrene microspheres followed by suppression of angiogenesis with the inhibitor of vascular endothelial growth factor receptor 2 (VEGF-R2) SU5416 results in transient, acute pulmonary hypertension that progresses into chronic PE with PH with sustained right ventricular systolic pressures exceeding 70 mmHg (chronic pulmonary embolism [CPE] model). This model is similar to the widely utilized hypoxia/SU5416 model with the exception that the "first hit" is PE. Rats with CPE have impaired right heart function characterized by reduced VO2 Max, reduced cardiac output, and increased Fulton index. None of these metrics are adversely affected by PE alone. Contrast-mediated CT imaging of lungs from rats with PE minus SU5416 show large increases in pulmonary vascular volume, presumably due to an angiogenic response to acute PE/PH. Co-treatment with SU5416 suppresses angiogenesis and produces the CTEPH-like phenotype. We report here that treatment of CPE rats with agonists for soluble guanylate cyclase, a source of cGMP which is in turn a signal for angiogenesis, markedly increases angiogenesis in lungs, and ameliorates the cardiac deficiencies in the CPE model. These results have implications for future development of therapies for human CTEPH.
Comparative analysis on the evolution of ecological carrying capacity between provinces during urbanization process in China

Effective ecological carrying capacity (ECC) is the precondition for implementing sustainable urbanization in China. However, different regions in a big country such as China have different ecological conditions and different development modes. Therefore, the evolution of ECC is different between different regions during the urbanization process in China. This study presents a comparative analysis on the evolution of ecological carrying capacity between provinces in the context of China. The ECC indicators in this study are classified into four dimensions, namely, water carrying capacity, land carrying capacity, environment carrying capacity and energy carrying capacity. Indicator data used for analysis are collected from 30 provinces in China. The entropy method is adopted to analyze the ECC performance during the years of 2006-2017. The ECC evolution is described into stable, fluctuated, declined and improved. This study shows that the evolution of ECC performance varies significantly between different provinces. The findings of this study help the Chinese government understand the different ECC evolution between provinces and provide policy-makers with valuable reference for formulating effective measures with considering different ecological conditions in different provinces, to improve the ECC performance across the country in China.
Identification of Pulsatilla chinensis (Bge.) Regel and look-alike species by UHPLC-TOF-MS using multivariate statistical analysis

Pulsatillae Radix, the root of Pulsatilla chinensis (Bge.) Regel, is recorded in the Pharmacopoeia of the People's Republic of China and has been widely used for its pharmacological activities, such as anti-inflammatory, antioxidant, antibacterial, antitumor, and cardiovascular benefits. However, there are several look-alike species that can be marketed as Pulsatillae Radix. To distinguish P. chinensis (Bge.) Regel from its look-alikes, viz. Pulsatilla cernua (Thunb.) Bercht et Opiz., Pulsatilla dahurica (Fisch.) Spreng., Anemone tomeutosa (Maxim.) Pei., and Rhaponticum uniflorum (L.) DC, we used ultra high performance liquid chromatography with time-of-flight mass spectrometry combined with principal component analysis to compare their chemical compositions. Four ions, a (RT 8.98 min, m/z 1381.6671), b (RT 10.64 min, m/z 1219.6143), c (RT 11.52 min, m/z 1217.5978), and d (RT 13.6 min, m/z 749.4463), from P. chinensis (Bge.) Regel were identified as potential chemical markers to distinguish it from look-alike species using an unsupervised statistical model combined with orthogonal partial least-squares discriminant analysis. The results of this study provide an effective method for identifying and distinguishing P. chinensis (Bge.) Regel from similar plants.
Can the incentives polices promote the diffusion of distributed photovoltaic power in China?

Government incentive policies play an important role in the promotion of distributed photovoltaic power. However, which policy is more effective for the diffusion of distributed photovoltaic power? This is a question that needs to be answered. Based on this, we combined the two-factor learning curve and system dynamics model to study the dynamic diffusion process of China's distributed photovoltaic power (DSP). The results show that (1) the coefficients of learning by doing and learning by researching for DSP are 0.0435 and 0.2971 respectively. This indicates that technological innovation caused by R&D expenditures in the DSP is the driving force for cost reduction. (2) Both demand-pull and technology-push policies contribute to the diffusion of DSP; (3) the effect of FIT policy on the diffusion of distributed photovoltaic technology is more significant than that of R&D policy; and the reduction of production cost of photovoltaic power industry by R&D policy is more significant than FIT policy.
Impact of the Burden of COVID-19 in Italy: Results of Disability-Adjusted Life Years (DALYs) and Productivity Loss

The WHO declared the novel coronavirus disease a pandemic, with severe consequences for health and global economic activity and Italy is one of the hardest hit countries. This study aims to assess the socio-economic burden of COVID-19 pandemic in Italy through the estimation of Disability-Adjusted Life Years (DALYs) and productivity loss. The observational study was based on data from official governmental sources collected since the inception of epidemic until 28 April 2020. DALYs for a disease combines the years of life lost due to premature mortality in the population and the years lost due to disability of the disease. In addition to DALYs, temporary productivity loss due to absenteeism from work and permanent productivity loss due to premature mortality were estimated using the Human Capital Approach. The total DALYs amount to 2.01 per 1000 persons. The total permanent productivity loss was around EUR 300 million while the temporary productivity loss was around EUR 100 million. This evaluation does not consider other economic aspects related to lockdown, quarantine of contacts, healthcare direct costs etc. The burden of disease methodology is functional metric for steering choices of health policy and allowing the government to be accountable for the utilization of resources.
Realizing High Thermoelectric Performance in p-Type SnSe Crystals via Convergence of Multiple Electronic Valence Bands

SnSe crystals have gained considerable interest for their outstanding thermoelectric performance. Here, we achieve excellent thermoelectric properties in Sn0.99-xPbxZn0.01Se crystals via valence band convergence and point-defect engineering strategies. We demonstrate that Pb and Zn codoping converges the energy offset between multiple valence bands by significantly modifying the band structure, contributing to the enhancement of the Seebeck coefficient. The carrier concentration and electrical conductivity can be optimized, leading to an enhanced power factor. The dual-atom point-defect effect created by the substitution of Pb and Zn in the SnSe lattice introduces strong phonon scattering, significantly reducing the lattice thermal conductivity to as low as 0.284 W m-1 K-1. As a result, a maximum ZT value of 1.9 at 773 K is achieved in Sn0.93Pb0.06Zn0.01Se crystals along the bc-plane direction. This study highlights the crucial role of manipulating multiple electronic valence bands in further improving SnSe thermoelectrics.
Coupling coordination and spatiotemporal dynamic evolution between social economy and water environmental quality - A case study from Nansi Lake catchment, China

Investigating the coordinated development between social economy and water environmental quality is an important basis for promoting the protection of water ecological environment. In this paper, a comprehensive index system was built to estimate the coupling coordination between social economy and water environmental quality subsystems in Nansi Lake catchment, China. Improved coupling coordination degree (CCD) model, scissors difference and projection pursuit model based on genetic algorithm were developed to investigate the spatiotemporal distribution and dynamic evolution mechanism of coupling coordination in social economy-water environment system. Results showed that: (1) Comprehensive level of two subsystems showed an overall uptrend, water environmental quality in the catchment was improved gradually with socio-economic development. (2) The status of coupling coordination between two subsystems had transformed from moderately unbalanced development (CCD of 0.265 in 2001) to favorably balanced development (CCD of 0.667 in 2017). The relatively low CCD in Heze generated the biggest restriction on the coordinated development between two subsystems, compared to Zaozhuang and Jining. (3) The speed difference between two subsystems showed a downward trend, the scissor difference was controlled effectively. And the social economy-water environmental quality system gradually tended to the coordinated development. Overall, the improved CCD model and scissors difference analysis in this paper may provide a new approach for empirical research on environment-economy nexus. We hope this study can help decision makers formulate measures to promote the coordinated development between social economy and water environmental quality subsystems.
Industrial structure transformation and provincial heterogeneity characteristics evolution of air pollution: Evidence of a threshold effect from China

As an important link between human economic activities and air quality, industrial structure is the key factor in resolving the contradiction between economic development and the environment. By constructing a panel threshold model between China's air pollution and the industrial structure, this study analyzes and explores how industrial transformation influences air pollution as well as its provincial heterogeneity characteristics evolution. The results show that industrial structure has a three-stage influential mechanism on NOx pollution and PM2.5 pollution, and a two-stage influential mechanism on SO2 pollution. A decrease in proportion of the secondary industry output in GDP can significantly reduce NOx pollution and SO2 pollution. Industrial structure can change the effects of economic development on air pollution. China' s industrial structure has been continuously upgraded in the past decade. In the process of industrial structure transformation, energy consumption has always been a major factor in aggravating air pollution, whereas technological innovation has been an important way to reduce air pollution. Urbanization is another major factor exacerbating air pollution in the provinces where the service industry has always dominated the local economy. Based on the above research, the government should formulate corresponding industrial structure adjustment policies to reduce air pollution according to the provincial heterogeneity of industrial structure affecting different pollutants.
Solving the duck curve in a smart grid environment using a non-cooperative game theory and dynamic pricing profiles

With the intermittency that comes with electricity generation from renewables, utilizing dynamic pricing will encourage the demand-side to respond in a smart way that would minimize the electricity costs and flatten the net electricity demand curve. Determining the optimal dynamic pricing profile that would leverage distributed storage to flatten the curve is a novel idea that needs to be studied. Moreover, the economic feasibility of utilizing distributed electrical energy storage is still not given in the literature. Therefore, in this paper, a novel way of solving a citywide dynamic model using a bilevel programming algorithm is introduced. The problem is developed as a novel non-cooperative Stackelberg game that utilizes air-conditioning systems and electrical storage through the end-users to determine the optimal dynamic pricing profile. The results show that the combined effect of utilizing demand-side air-conditioning systems and distributed storage together can flatten the curve while employing the optimal dynamic pricing profile. An economic study is performed to determine the economic feasibility of 20 different cases with different battery designs and the level of solar penetration. Three metrics were used to evaluate the economic performance of each case: the levelized cost of storage, the levelized cost of energy, and the simple payback period. Most cases had levelized cost of storage values lower than 0.457 $/kWh, which is the lower bound available in the literature. Seven out of 16 cases have a simple payback period shorter than the lifetime of the system (25 years). The case with a 100 MW PV power plant and a battery storage of size 597 MWh, was found to be the most promising case with a simple payback period of 12.71 years for the photovoltaic plant and 19.86 years for the demand-side investments.
The impact of financial development and globalization on environmental quality: evidence from South Asian economies

Climate change resulting from the higher concentration of greenhouse gases in the atmosphere is a threat to the sustainability of life on earth. To ensure sustainable development, the study analyzed the impact of financial development, globalization, and energy use on the environmental quality of South Asian economies over the period 1990-2014. To account for cross-sectional dependence, Breusch-Pagan-Lagrange multiplier, Pesaran-scaled Lagrange multiplier, bias-corrected-scaled Lagrange multiplier, and Pesaran cross-sectional dependence tests are used. The second-generation tests are used to determine the stationarity level of the variables. Furthermore, the Westerlund panel cointegration test confirms cointegration among the variables. For long-run association, fully modified ordinary least squares, dynamic ordinary least squares, and pooled mean group estimators are used. The results suggest that financial development contributes to carbon emissions, whereas globalization has the potential to control emissions. The study also used Dumitrescu and Hurlin's (2012) panel causality test to explore the causal relationship among the variables. Unidirectional causality is observed from economic growth, globalization, and financial development to environmental degradation and from emissions to energy use, respectively. As financial development deteriorates environmental quality, therefore, the government should monitor the disbursement of loans for research and development, green financing, and efficient production that reduce resource consumption and improves environmental quality. Financial development should not compromise environmental quality and endanger sustainability. Furthermore, South Asian countries should promote globalization to support the inflow of green technologies to enhance environmental quality.
Spatial distribution and source analysis of heavy metals in soils influenced by industrial enterprise distribution: Case study in Jiangsu Province

Heavy metal pollution is frequent in China and has received increasing attention globally. This study investigated the influence of Chinese industrialization and urbanization on soil environmental quality. Soil samples from Jiangsu Province were collected, the Cd, Pb, Cr, Cu, Zn, Hg, and As contents were measured, and their spatial variability structure, spatial distribution pattern, and pollution degree were analyzed. The mean values of Hg, Cd, Pb, Cr, Cu, and As were all higher than the background values in Jiangsu Province. Cr and As levels represented moderate pollution, Cu, Zn, Cd, and Pb represented mild pollution, Cr and As represented slight pollution, and Hg was not a pollutant. Spatial distribution patterns were both zonal and concentrated in nature. High concentrations of heavy metals were distributed in developed cities and industrial parks along the Yangtze River. Soil heavy metal pollution showed a decreasing trend from south to north, consistent with the economic gradient. Industrialization had the greatest influence on the spatial heterogeneity of heavy metal pollution. Cr, Cu, Zn, and As were affected by both natural and anthropogenic sources, while Cd and Pb were mainly affected by the latter. Hg was mainly derived from industrial activities such as petrochemical production. There was spatial consistency between industrial enterprise distribution and soil heavy metal pollution with a tendency toward composite pollution accumulated by multiple elements in the soil surrounding industries. (C) 2019 Published by Elsevier B.V.
Using artificial intelligence technology to fight COVID-19: a review

In late December 2019, a new type of coronavirus was discovered, which was later named severe acute respiratory syndrome coronavirus 2(SARS-CoV-2). Since its discovery, the virus has spread globally, with 2,975,875 deaths as of 15 April 2021, and has had a huge impact on our health systems and economy. How to suppress the continued spread of new coronary pneumonia is the main task of many scientists and researchers. The introduction of artificial intelligence technology has provided a huge contribution to the suppression of the new coronavirus. This article discusses the main application of artificial intelligence technology in the suppression of coronavirus from three major aspects of identification, prediction, and development through a large amount of literature research, and puts forward the current main challenges and possible development directions. The results show that it is an effective measure to combine artificial intelligence technology with a variety of new technologies to predict and identify COVID-19 patients.
The impact of international sanctions on environmental performance

This paper studies the impact of international sanctions on environmental performance by using the panel fixed effect model for 22 sanctioned states over the period 2002-2016 while considering several cases of international sanctions (unilateral, plurilateral, U.S., EU, UN, economic, and intensity) and measures environmental performance by the Environmental Performance Index (EPI). Overall, we find that the imposition of unilateral, plurilateral, US., EU, and economic sanctions has a significantly negative impact on EPI, dropping their EPI scores respectively by 0.114,0.060,0.045,0.084, and 0.063, while a level increase of intensity results in EPI dropping by 0.028. Moreover, the simultaneous-equation models and Sobel tests show that the intermediate effect about GDP between international sanctions on environmental performance is significantly negative. We believe that our empirical findings can contribute to policy formulation by those sanctioned countries. (C) 2020 Elsevier B.V. All rights reserved.
Paeonol Ameliorates Cuprizone-Induced Hippocampal Demyelination and Cognitive Deficits through Inhibition of Oxidative and Inflammatory Events

Multiple sclerosis (MS) is a chronic and inflammatory disorder of the central nervous system with autoimmune nature that is typified by varying degrees of demyelination and axonal damage. Paeonol is an active ingredient in some medicinal plants with anti-inflammatory and neuroprotective property. This study was conducted to reveal whether paeonol can alleviate hippocampal demyelination and cognitive deficits in cuprizone-induced murine model of demyelination as a model of MS. C57BL/6 mice received oral cuprizone (400 mg/kg) for 6 weeks, and paeonol was administered p.o. at two doses of 25 or 100 mg/kg, starting from the second week post-cuprizone for 5 weeks. After assessment of learning and memory in different tasks, oxidative stress and inflammation were evaluated besides immunohistochemical assessment of hippocampal myelin basic protein (MBP). Paeonol (100 mg/kg) properly ameliorated cognitive deficits in Y maze, novel object discrimination (NOD) test, and Barnes maze with no significant improvement of performance in passive avoidance task. In addition, paeonol treatment at the higher dose was also associated with partial restoration of hippocampal level of oxidative stress and inflammatory markers including MDA, ROS, GSH, SOD, catalase, NF-kB, and TNF. Besides, paeonol improved MMP as an index of mitochondrial integrity and health and reduced MPO as a factor of neutrophil infiltration. Furthermore, paeonol treatment prevented hippocampal MBP immunoreactivity, indicating its prevention of demyelination. In conclusion, the current study showed the preventive effect of paeonol against cuprizone-induced demyelination and cognitive deficits through reversing most oxidative stress- and inflammation-related parameters in addition to its improvement of mitochondrial health.
Implementing Circular Economy in municipal solid waste treatment system using P-graph

Municipal solid waste (MSW) is one of the issues associated with the growth of economic and urban population. The aim of this study is to develop an integrated design of waste management systems in support of a Circular Economy by P-graph (a bipartite graphical optimisation tool) as an effective decision support tool. The case study considers four MSW compositions based on different country income levels. Solving the P-graph model identifies the most suitable treatment approaches, considering the economic balance between the main operating cost, type, yield, quality of products, as well as the GHG emission (externality cost). The identification of near-optimal solutions by P-graph is useful in dealing with the trade-offs between conflicting objectives, e.g. local policy and practical implementation, that are difficult to monetise. For a lower-income country, the optimal solution includes a combination of at source separation, recycling, incineration (heat, electricity), anaerobic digestion (biofuel, digestate) and the landfill. It avoids an estimated 411 kg CO2eq/t of processed MSW and achieves a potential profit of 42 SIC/t of processed MSW. The optimisation generally favours mechanical biological treatment as the country income level rises, which affects the composition of the MSW. The relative prices of biofuel, electricity and heat (>20%) cause a significant impact on the highest-ranking treatment structure and overall profit. This study shows that the developed framework by P-graph is an effective tool for MSW systems planning. For future study, localised data inputs can be fed into the proposed framework for a customised solution and economic feasibility assessment. (C) 2019 Elsevier B.V. All rights reserved.