ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Bacterial diversity evolution process based on physicochemical characteristics of sludge treating hydroquinone during acclimation

Hydroquinone is one of the main pollutants in coal-gasification wastewater, which is biologically toxic and difficult to remove. The aerobic biodegradation rate, organic toxicity, and microbial community structure at different acclimation stages of degradation of hydroquinone by activated sludge were investigated. In each acclimation cycle, the removal of hydroquinone reached 100% after 5 days, indicating that high-concentration hydroquinone in the activated sludge could be completely biodegraded. When the microbial flora was inhibited by the influent hydroquinone, the enzyme system experienced stress conditions and led to the secretion of secondary metabolites, extracellular protein of 5-10 kDa mainly contributing to the sludge organic toxicity. Microbial diversity analysis showed that with the increase of the concentration of hydroquinone, β-Proteus bacteria such as Azoarcus and Dechloromonas gradually accumulated, which improved the removal of hydroquinone with aerobic activated sludge in the sequencing batch reactor (SBR) system. As the inhibition degree exceeded the appropriate tolerance range of microorganisms, bacteria would secrete much more secondary metabolites, and the organic toxicity of sludge would reach a relatively high level.
Modeling and performance analysis of a fully solar-powered stand-alone sweeping gas membrane distillation desalination system for island and coastal households

Solar-powered membrane distillation is a promising desalination technology. This study newly proposed a solar-powered stand-alone sweeping gas membrane distillation desalination system to provide flexible fresh water for remote island households without reliable infrastructure, such as water and power supplies. Solar energy is the only source of energy to drive the system. A solar thermal collector is installed to provide thermal energy and a solar photovoltaic array is installed to supply direct current power respectively. To evaluate the system performance, a system-scale mathematical model was established and validated by test results under real meteorological conditions. The performance of the major components of the system, including the thermal efficiency of the solar thermal collector, the electrical efficiency of the photovoltaic array, the humidification efficiency of the hollow-fiber-membrane module and the dehumidification efficiency of the condenser, was analyzed. The effects of structural parameters, such as solar collector area, solar photovoltaic area, and desalination operating parameters, on system freshwater yield and Gained Output Ratio were investigated. In addition, the system's daily freshwater production in each month was predicted under local weather conditions of Hong Kong. In summary, for the proposed system, the average daily freshwater production is between 9.98 kg/d and 23.26 kg/d (9.98 kg/d in January and 23.26 kg/d in July), which is enough to meet the daily drinking water demand of a typical family of four (two adults and two children). The ratio of solution flow rate to air flow rate should be selected in the range of 4.0-6.0 to obtain the optimal freshwater yield. The average thermal efficiency of the solar thermal collector is 50%, which is about three times as much as the power conversion efficiency of the solar photovoltaic panels (about 15%). Thus, the solar collector area shows a more significant impact on the system performance than the photovoltaic area. The final water production cost of the system is about 18.34 $/m(3). The system proposed in this paper as a small-scale fully solar-powered desalination system is attractive to provide a flexible and reliable fresh water supply for island and coastal households.
Musical and linguistic syntactic processing in agrammatic aphasia: An ERP study

Language and music rely on complex sequences organized according to syntactic principles that are implicitly understood by enculturated listeners. Across both domains, syntactic processing involves predicting and integrating incoming elements into higher-order structures. According to the Shared Syntactic Integration Resource Hypothesis (SSIRH; Patel, 2003), musical and linguistic syntactic processing rely on shared resources for integrating incoming elements (e.g., chords, words) into unfolding sequences. One prediction of the SSIRH is that people with agrammatic aphasia (whose deficits are due to syntactic integration problems) should present with deficits in processing musical syntax. We report the first neural study to test this prediction: event-related potentials (ERPs) were measured in response to musical and linguistic syntactic violations in a group of people with agrammatic aphasia (n=7) compared to a group of healthy controls (n=14) using an acceptability judgement task. The groups were matched with respect to age, education, and extent of musical training. Violations were based on morpho-syntactic relations in sentences and harmonic relations in chord sequences. Both groups presented with a significant P600 response to syntactic violations across both domains. The aphasic participants presented with a reduced-amplitude posterior P600 compared to the healthy adults in response to linguistic, but not musical, violations. Participants with aphasia did however present with larger frontal positivities in response to violations in both domains. Intriguingly, extent of musical training was associated with larger posterior P600 responses to syntactic violations of language and music in both groups. Overall, these findings are not consistent with the predictions of the SSIRH, and instead suggest that linguistic, but not musical, syntactic processing may be selectively impaired in stroke-induced agrammatic aphasia. However, the findings also suggest a relationship between musical training and linguistic syntactic processing, which may have clinical implications for people with aphasia, and motivates more research on the relationship between these two domains.
Monitoring the incidence of Xylella fastidiosa infection in olive orchards using ground-based evaluations, airborne imaging spectroscopy and Sentinel-2 time series through 3-D radiative transfer modelling

Outbreaks of Xylella fastidiosa (Xf) in Europe generate considerable economic and environmental damage, and this plant pest continues to spread. Detecting and monitoring the spatio-temporal dynamics of the disease symptoms caused by Xf at a large scale is key to curtailing its expansion and mitigating its impacts. Here, we combined 3-D radiative transfer modelling (3D-RTM), which accounts for the seasonal background variations, with passive optical satellite data to assess the spatio-temporal dynamics of Xf infections in olive orchards. We developed a 3D-RTM approach to predict Xf infection incidence in olive orchards, integrating airborne hyperspectral imagery and freely available Sentinel-2 satellite data with radiative transfer modelling and field observations. Sentinel-2A time series data collected over a two-year period were used to assess the temporal trends in Xf-infected olive orchards in the Apulia region of southern Italy. Hyperspectral images spanning the same two-year period were used for validation, along with field surveys; their high resolution also enabled the extraction of soil spectrum variations required by the 3D-RTM to account for canopy background effect. Temporal changes were validated with more than 3000 trees from 16 orchards covering a range of disease severity (DS) and disease incidence (DI) levels. Among the wide range of structural and physiological vegetation indices evaluated from Sentinel-2 imagery, the temporal variation of the Atmospherically Resistant Vegetation Index (ARVI) and Optimized Soil-Adjusted Vegetation Index (OSAVI) showed superior performance for DS and DI estimation (r(VALUES)(2) > 0.7, p < 0.001). When seasonal understory changes were accounted for using modelling methods, the error of DI prediction was reduced 3-fold. Thus, we conclude that the retrieval of DI through model inversion and Sentinel-2 imagery can form the basis for operational vegetation damage monitoring worldwide. Our study highlight the value of interpreting temporal variations in model retrievals to detect anomalies in vegetation health.
Causal chain of haze decoupling efforts and its action mechanism: Evidence from 30 provinces in China

Under the premise of ensuring economic growth, how to achieve the maximum reduction of PM2.5 has become one of the important tasks in China. In this study, we investigated the influencing factors of PM2.5 by employing the Logarithmic Mean Divisia Index (LMDI). The sensitivity of PM2.5 to various emission reduction factors was also explored. Then, by constructing an indicator of PM2.5 decoupling efforts (PDE), PDE of different regions and the degree of efforts caused by various factors were explored. Finally, the spatiotemporal heterogeneity of internal and external factors influencing PDE was explored using the Geographically and Temporally Weighted Regression Model (GTWR). The main results are as follows. (1) The emission coefficient was the main negative driving factor of changes in PM2.5, and per capita GDP was the main positive driving factor. (2) The sensitivity of PM2.5 to emission reduction factors in most regions followed the ranking: emission coefficient > energy intensity > population. In the analysis of the sensitivity of PM2.5 to various emission reduction factors throughout the country, the sensitivity of the emission coefficient was highest in Shandong province. (3) From 2000 to 2014, all provinces made decoupling efforts (PDE>0), among which PDE in Guizhou province were the largest. According to various indicators of decoupling efforts, the contribution of decoupling effort of emission coefficient was the largest. (4) All influencing factors exhibited spatiotemporal heterogeneity. In most regions, the interaction between environmental regulation and industrial structure had a positive impact on PDE, which verifies the existence of the "Porter Hypothesis". However, in some central and western regions, this interaction exhibited a dynamic change from negative to positive. (C) 2019 Elsevier Ltd. All rights reserved.
Testing the environmental Kuznets Curve hypothesis for E-waste in the EU28+2 countries

This paper investigates the Kuznets Curve hypothesis for e-waste using a balanced panel of 30 European countries (EU28 + 2) over the period 2000e2016. To account for the dynamic and the time-invariant characteristics, we employed the generalized method of moments (GMM) estimator as a core method and used the 2SLS estimator and cross-section method to analyze the robustness of results. We found that the Environmental Kuznets Curve (EKC) hypothesis is well supported for e-waste generated in the EU28 + 2 area. In addition, this relationship is robust and is not sensitive to the choice of control variables and the estimation methods. All three methods evidenced EKC in e-waste for the studied European area. This reveals that e-waste generation increases with an increasing GDP up to a certain GDP level (turning point), but thereafter decreases despite further economic growth. While this suggests that ewaste generation and the related adverse environmental impacts will decline at elevated GDP levels, the results of this study highlight that the turning point occurs at rather exceptionally high levels of GDP. The evidence of EKC in e-waste contributes to better understanding the interlinkage between economic growth and e-waste generation, but it does not diminish the urgent need to implement effective e-waste collection and valorization schemes. (C) 2020 Elsevier Ltd. All rights reserved.
Urbanization and energy consumption effects on carbon dioxide emissions: evidence from Asian-8 countries using panel data analysis

The developing world is facing pivotal challenges in recent times. Among these, global warming has ominous repercussions on every segment of society, thus tracing its underlying causes is imperative. This research attempts to investigate the impact of urbanization and energy consumption on carbon dioxide emissions (CO2) for a panel of 8 Asian countries (Bangladesh, China, India, Indonesia, Malaysia, Nepal, Pakistan, and Sri Lanka) over the period 1982 to 2017. The analyses are executed using panel co-integration and Granger causality techniques. The main findings of panel co-integration reveal a long-run relationship between urbanization, energy consumption, and CO2 emissions. Furthermore, the results indicate a positive and significant impact of urbanization and energy consumption on CO2 emissions, indicating that urban development and high energy consumptions are barriers to improve environmental quality in the long run. The results also highlight bi-directional causality between energy consumption and urbanization, while unidirectional causality exists between energy consumption and CO2 emissions. Based on the obtained results, this study offers useful policy implications for plummeting carbon emissions.
Developmental toxicity and neurotoxicity assessment of R-, S-, and RS-propylene glycol enantiomers in zebrafish (Danio rerio) larvae

Propylene glycol (PG) is widely used in the foods, pharmaceuticals, oil industry, animal feed, cosmetics and other industries. Because of the existence of a chiral carbon center, PG forms R (Rectus)- and S (Sinister)-enantiomers. Currently, the toxicity study of its R-, S-enantiomers is still very scarce. In this study, we have assessed the developmental toxicity and neurotoxicity of the R-, S-, and RS-PG enantiomers in zebrafish larvae. We found that exposure to R-, S-, and RS-PG enantiomers did not significantly affect the basic developmental endpoints of embryos or larvae (i.e., embryonic movement, hatching, mortality, malformation, heartbeat, body length), indicating that R-, S-, and RS-PG exposures did not exhibit the basic developmental toxicity in zebrafish larvae. The toxicity of three enantiomers was lower than that of ethanol, and there was no significant difference between them. However, R-, S-, and RS-PG exposures with high doses could significantly change the eye diameter and locomotor activity of larval zebrafish, indicating that R-, S-, and RS-PG enantiomers of high doses could potentially exhibit the neurotoxicity and ocular developmental toxicity in zebrafish larvae. Therefore, the potential neurotoxicity and ocular developmental toxicity of R-, S-, and RS-PG enantiomers for infants and toddlers should be considered.
Highly active Fenton-like catalyst derived from solid waste-iron ore tailings using wheat straw pyrolysis

The pollutants degradation rate of iron ore tailings-based heterogeneous catalysts is the main factor limiting its application. Herein, an iron ore tailings-based Fenton-like catalyst (I/W(3:1)-900-60) with a relatively fast catalysis rate was constructed by co-pyrolysis (900°C, 60 min holding time) of iron ore tailings and wheat straw with a mass ratio of 3:1. With wheat straw blending, the generated I/W(3:1)-900-60 presented a larger surface area (24.53 m2/g), smaller pore size (3.76 nm), reduced iron species (Fe2+ from magnetic), and a higher catalytic activity (0.0229 min-1) than I-900-60 (1.32 m2/g, 12.87 nm, 0.012 min-1) pyrolyzed using single iron ore tailing under the same pyrolysis conditions. In addition, biochar and iron ore tailings in I/W(3:1)-900-60 were tightly combined through chemical bonding. The optimal catalyst remains active after three cycles, indicating its catalytic stability and recyclability. The good Fenton-like methylene blue degradation efficiency of I/W(3:1)-900-60 was ascribed to the sacrificial role of biochar, as well as the electron transfer between biochar and iron active sites or the redox cycles of ≡Fe3+/Fe2+. This finding provides a facile construction strategy for highly active iron ore tailings-based Fenton-like catalyst and thereby had a great potential application in wastewater treatment.
Low carbon roadmap of residential building sector in China: Historical mitigation and prospective peak

Fast-growing carbon emissions from the residential building sector are a hindrance for China to achieve its 2030 emission peak goal. To identify future low carbon roadmaps of residential buildings, this study is the first to assess the historical carbon mitigation and simulate the energy and emission peaks of China's residential building sector using a dynamic emission scenario. It shows that the emission mitigation of the residential building sector during 2000-2015 is 1.817 (+/- 0.643) billion tons of carbon dioxide (BtCO(2)), and the normal distribution-based scenario simulation demonstrates that the residential building sector will achieve its carbon emission peak in 2037 (+/- 4) with a peak value of 1.419 (+/- 0.081) BtCO(2). The sensitivity analysis reveals that the impacts of floor space per capita and energy intensity of urban residential buildings are the most significant for the uncertainty of emission peaks. A strict energy demand benchmark of the residential building sector suggests a control at 0.661 billion tons of standard coal equivalent (Btce), and its peaking time is estimated for 2035, which is 5 years ahead of the business-as-usual scenario, with energy savings of 0.042 Btce. For the earliest peaking time, if the residential building sector aims to achieve its emission peak before 2030, the emission peak should be controlled at 1.258 BtCO(2). Overall, this paper can assist the government in more accurate and feasible building emission mitigation strategies. Moreover, the results provide a more powerful decision-making reference in issuing targeted and feasible strategies for future residential building emission mitigation.
Harnessing the potential of machine learning for advancing "Quality by Design" in biomanufacturing

Ensuring consistent high yields and product quality are key challenges in biomanufacturing. Even minor deviations in critical process parameters (CPPs) such as media and feed compositions can significantly affect product critical quality attributes (CQAs). To identify CPPs and their interdependencies with product yield and CQAs, design of experiments, and multivariate statistical approaches are typically used in industry. Although these models can predict the effect of CPPs on product yield, there is room to improve CQA prediction performance by capturing the complex relationships in high-dimensional data. In this regard, machine learning (ML) approaches offer immense potential in handling non-linear datasets and thus are able to identify new CPPs that could effectively predict the CQAs. ML techniques can also be synergized with mechanistic models as a 'hybrid ML' or 'white box ML' to identify how CPPs affect the product yield and quality mechanistically, thus enabling rational design and control of the bioprocess. In this review, we describe the role of statistical modeling in Quality by Design (QbD) for biomanufacturing, and provide a generic outline on how relevant ML can be used to meaningfully analyze bioprocessing datasets. We then offer our perspectives on how relevant use of ML can accelerate the implementation of systematic QbD within the biopharma 4.0 paradigm.
Magnetic adsorbent developed with alkali-thermal pretreated biogas slurry solids for the removal of heavy metals: optimization, kinetic, and equilibrium study

Discharge of effluents containing heavy metal without adequate treatment causes contamination of water resources and creates environmental and health issues. Adsorption could be applied to remediate heavy metals from wastewater effectively. In this study, a low-cost adsorbent was prepared by magnetic modification of pretreated biogas slurry solids (BSS) to remove heavy metals such as Cu2+, Cd2+, and Pb2+. The temperature (423 K) and time (1.5 h) of pretreatment, the BSS to KOH ratio (1:10 w/v), and the ratio of magnetic iron nanoparticle (MIN) to pretreated BSS (PSS) (1:2 w/w) were optimized for the preparation of adsorbent. The magnetically modified pretreated biogas slurry solid (MMPSS) adsorbent was characterized by BET isotherm, FTIR, XRD, FESEM, VSM, and EDX analysis. MMPSS attained equilibrium at 60 min and showed an adsorption capacity of 26.84 mg/g, 24.79 mg/g, and 23.86 mg/g with removal percentages 89.46%, 82.63%, and 79.54% for Cu2+, Cd2+, and Pb2+, respectively, at 310 K and pH 6 with an initial concentration of 150 mg/L. The adsorption process followed a pseudo second-order model with an R2 value above 0.9 for all metals with a well-approaching equilibrium pattern. The good fit of experimental data by the Langmuir isotherm model implied monolayer adsorption. The metal ions adsorbed onto MMPSS were able to desorb effectively in the presence of HCl and retained 83.01%, 84.66%, and 81.83% of the initial adsorption capacity for Cu2+, Cd2+, and Pb2+ respectively after 5 consecutive cycles.
Is a liveable city a healthy city? Health impacts of urban and transport planning in Vienna, Austria

Each year, The Economist Intelligence Unit (EIU) computes the Global Liveability Index and determines the most liveable cities around the world. Vienna, Austria, was ranked by the EIU as the most liveable city worldwide in 2018 and 2019. However, the relationship between a liveable as well as healthy and environmentally-just city has not been previously explored. To explore whether the most liveable city is also a healthy and environmentally-just one, we estimated the premature mortality burden related to non-compliance with international exposure level recommendations for physical activity (PA), air pollution (PM2.5 and NO2), road traffic noise, green space and heat for Vienna, as well as its distribution by socioeconomic status (SES). We applied the Urban and TranspOrt Planning Health Impact Assessment (UTOPHIA) methodology and estimated the annual mortality, life expectancy (LE) and economic impact of non-compliance with exposure guidelines for the Viennese adult population >= 20 years. We compared current with recommended exposure levels, quantified the association between exposures and mortality and calculated attributable health impact fractions. Eight percent of premature mortality (i.e. 1239 deaths, 95% CI: 679-1784) was estimated to be attributable to non-compliance with the recommended exposure levels. Seventy-six percent of the attributable premature mortality was due to PM2.5 exposure and insufficient PA. Non-compliance also resulted in an average of 199 days of LE lost for the adult population (95% CI: 111-280) and an economic impact of 4.6 (95% CI: 2.5-6.7) billion 2015(sic) annually. Overall, residents of lower SES neighbourhoods faced higher risk of premature mortality due to higher exposure to NO2, road traffic noise, heat and less green space. Despite high liveability standards according to EIU definition, a considerable premature mortality burden was attributable to non-compliance with exposure recommendations, and socioeconomic inequalities were estimated. Although the exposure attributable mortality burden was lower than in other European cities and local Viennese policies favour the reduction of motorized traffic, alongside the promotion of active and public transport and urban greening, there is room for further alignment of liveability, environmental health and justice objectives.