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"SATTAR, Abdul"
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Effects of urbanization and nonrenewable energy on carbon emission in Africa
This study investigates the impact of urbanization and nonrenewable energy consumption on carbon emissions. The context of the analysis is 54 African Union countries from 1996 to 2019. For estimation, we use panel quantile regression (PQR) and fully modified ordinary least squares (FMOLS). Our regression results demonstrate that there is a positive correlation between urbanization and CO
2
emission. Further, our empirical results confirmed that nonrenewable energy consumption increases environmental pollution in African Union countries. The outcomes demonstrate the EKC hypothesis because at the initial stage of development, when economic growth increases, environmental pollution increases; after a threshold point, environmental pollution decreases as economic growth increases. It can find an inverted U-shaped relationship between economic growth and CO
2
emission. The findings also show that urbanization should be planned; otherwise, it can lead to environmental degradation in the long run. Africa continent takes strict action and builds a blueprint for efficient and effective energy production and consumption. The only solution to achieve green growth in Africa is to shift from fossil fuel energy supply to renewable energy supply.
Journal Article
Differential effects of dietary flavonoids on adipogenesis
ProposeObesity is a fast growing epidemic worldwide. During obesity, the increase in adipose tissue mass arise from two different mechanisms, namely, hyperplasia and hypertrophy. Hyperplasia which is the increase in adipocyte number is characteristic of severe obese patients. Recently, there has been much interest in targeting adipogenesis as therapeutic strategy against obesity. Flavonoids have been shown to regulate several pathways and affect a number of molecular targets during specific stages of adipocyte development.MethodsPresently, we provide a review of key studies evaluating the effects of dietary flavonoids in different stages of adipocyte development with a particular emphasis on the investigations that explore the underlying mechanisms of action of these compounds in human or animal cell lines as well as animal models.ResultsFlavonoids have been shown to regulate several pathways and affect a number of molecular targets during specific stages of adipocyte development. Although most of the studies reveal anti-adipogenic effect of flavonoids, some flavonoids demonstrated proadipogenic effect in mesenchymal stem cells or preadipocytes.ConclusionThe anti-adipogenic effect of flavonoids is mainly via their effect on regulation of several pathways such as induction of apoptosis, suppression of key adipogenic transcription factors, activation of AMPK and Wnt pathways, inhibition of clonal expansion, and cell-cycle arrest.
Journal Article
CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles
Motivation
Ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big concern during drug development in the pharmaceutical industry. Blockade of hERG channels may cause prolonged QT intervals that potentially could lead to cardiotoxicity. Various in-silico techniques including deep learning models are widely used to screen out small molecules with potential hERG related toxicity. Most of the published deep learning methods utilize a single type of features which might restrict their performance. Methods based on more than one type of features such as DeepHIT struggle with the aggregation of extracted information. DeepHIT shows better performance when evaluated against one or two accuracy metrics such as negative predictive value (NPV) and sensitivity (SEN) but struggle when evaluated against others such as Matthew correlation coefficient (MCC), accuracy (ACC), positive predictive value (PPV) and specificity (SPE). Therefore, there is a need for a method that can efficiently aggregate information gathered from models based on different chemical representations and boost hERG toxicity prediction over a range of performance metrics.
Results
In this paper, we propose a deep learning framework based on step-wise training to predict hERG channel blocking activity of small molecules. Our approach utilizes five individual deep learning base models with their respective base features and a separate neural network to combine the outputs of the five base models. By using three external independent test sets with potency activity of IC
50
at a threshold of 10
μ
m, our method achieves better performance for a combination of classification metrics. We also investigate the effective aggregation of chemical information extracted for robust hERG activity prediction. In summary, CardioTox net can serve as a robust tool for screening small molecules for hERG channel blockade in drug discovery pipelines and performs better than previously reported methods on a range of classification metrics.
Journal Article
A Novel Algorithm for Studying the Effects of Squeezing Flow of a Casson Fluid between Parallel Plates on Magnetic Field
In this paper, the magneto hydrodynamic (MHD) squeezing flow of a non-Newtonian, namely, Casson, fluid between parallel plates is studied. The suitable one of similarity transformation conversion laws is proposed to obtain the governing MHD flow nonlinear ordinary differential equation. The resulting equation has been solved by a novel algorithm. Comparisons between the results of the novel algorithm technique and other analytical techniques and one numerical Range-Kutta fourth-order algorithm are provided. The results are found to be in excellent agreement. Also, a novel convergence proof of the proposed algorithm based on properties of convergent series is introduced. Flow behavior under the changing involved physical parameters such as squeeze number, Casson fluid parameter, and magnetic number is discussed and explained in detail with help of tables and graphs.
Journal Article
Bibliometric Analysis of Trends in Biomass for Bioenergy Research
This paper aims to provide a bibliometric analysis of publication trends on the themes of biomass and bioenergy worldwide. A wide range of studies have been performed in the field of the usage of biomass for energy production, in order to contribute to the green transition from fossil fuels to renewable energies. Over the past 20 years (from 2000 to 2019), approximately 10,000 articles have been published in the “Agricultural and Biological Sciences” field on this theme, covering all stages of production—from the harvesting of crops to the particular type of energy produced. Articles were obtained from the SCOPUS database and examined with a text mining tool in order to analyze publication trends over the last two decades. Publications per year in the bioenergy theme have grown from 91 in 2000 to 773 in 2019. In particular the analyses showed how environmental aspects have increased their importance (from 7.3% to 11.8%), along with studies related to crop conditions (from 10.4% to 18.6%). Regarding the use of energy produced, growing trends were recognized for the impact of biofuels (mentions moved from 0.14 times per article in 2000 to 0.38 in 2019) and biogases (from 0.14 to 0.42 mentions). Environmental objectives have guided the interest of researchers, encouraging studies on biomass sources and the optimal use of the energy produced. This analysis aims to describe the research evolution, providing an analysis that can be helpful to predict future scenarios and participation among stakeholders in the sector.
Journal Article
PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features
Protein–peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and drug discovery efforts. Despite a significant increase in the availability of protein–peptide complexes, experimental methods for studying these interactions remain laborious, time-consuming, and expensive. Computational methods offer a complementary approach but often fall short in terms of prediction accuracy. To address these challenges, we introduce PepCNN, a deep learning-based prediction model that incorporates structural and sequence-based information from primary protein sequences. By utilizing a combination of half-sphere exposure, position specific scoring matrices from multiple-sequence alignment tool, and embedding from a pre-trained protein language model, PepCNN outperforms state-of-the-art methods in terms of specificity, precision, and AUC. The PepCNN software and datasets are publicly available at
https://github.com/abelavit/PepCNN.git
.
Journal Article
Advancing Sustainable Energy: Environmental and Economic Assessment of Plastic Waste Gasification for Syngas and Electricity Generation Using Life Cycle Modeling
The explosion of plastic waste generation, approaching 400 million tons per year, has created a worldwide environmental crisis that conventional waste management systems cannot handle. This problem can be solved through gasification, which converts nonrecyclable plastics to syngas with potential applications in electricity generation and synthetic fuel production. This study investigates whether syngas production from plastic waste by gasification is environmentally and economically feasible. Environmental impacts were assessed through a life cycle assessment framework using a life cycle impact assessment approach, ReCiPe 2016, with 10 midpoint/endpoint categories. Midpoint results of the baseline scenario with grid-mix electricity revealed climate change (GWP) of 775 kg CO2 equivalent and fossil depletion potential (FDP) of 311 kg oil equivalent per ton of plastic waste. Meanwhile, a solar scenario showed GWP as 435 kg CO2 equivalent and FDP as 166 kg oil equivalent per ton of plastic waste. Switching to solar energy cut GWP 44% and FDP 47%, respectively. However, the tradeoffs were higher human toxicity potential (HTP) and marine ecotoxicity potential (METP) due to upstream material extraction of renewable systems, respectively. Among environmental performance drivers, electricity inputs and operating materials were identified through sensitivity and uncertainty analyses. Syngas production from a plant of 50 tons per day can yield electricity sales revenue of USD 4.79 million, excluding USD 4.05 million in operational expenditures. Financial indicators like a 2.06-year payback period, USD 5.32 million net present value over a 20-year project life, and 38.2% internal rate of return indicate the profitability of the system. An external cost analysis showed emissions-related costs of USD 26.43 per ton of plastic waste processed, dominated by CO2 and NOx emissions. Despite these costs, the avoided impacts of less landfilling/incineration and electricity generation support gasification. Gasification should be promoted as a subsidy and incentive by policymakers for wider adoption and integration into municipal waste management systems. Findings show it can be adapted to global sustainability goals and circular economy principles while delivering strong economic returns. The study findings also contribute to several Sustainable Development Goals (SDGs), for instance, SDG 7 by promoting clean energy technologies, SDG 12 by implementing circular economy, and SDG 13 by reducing greenhouse gas (GHG) emissions.
Journal Article
Terminal drought and heat stress alter physiological and biochemical attributes in flag leaf of bread wheat
Heat stress along with low water availability at reproductive stage (terminal growth phase of wheat crop) is major contributing factor towards less wheat production in tropics and sub-tropics. Flag leaf plays a pivotal role in assimilate partitioning and stress tolerance of wheat during terminal growth phase. However, limited is known about biochemical response of flag leaf to combined and individual heat and drought stress during terminal growth phase. Therefore, current study investigated combined and individual effect of terminal drought and heat stress on water relations, photosynthetic pigments, osmolytes accumulation and antioxidants defense mechanism in flag leaf of bread wheat. Experimental treatments comprised of control, terminal drought stress alone (50% field capacity during reproductive phase), terminal heat stress alone (wheat grown inside plastic tunnel during reproductive phase) and terminal drought stress + terminal heat stress. Individual and combined imposition of drought and heat stresses significantly (p≤0.05) altered water relations, osmolyte contents, soluble proteins and sugars along with activated antioxidant defensive system in terms of superoxide dismutase (SOD), peroxidase (POD) and ascorbate peroxidase (APX). Turgor potential, POD and APX activities were lowest under individual heat stress; however, these were improved when drought stress was combined with heat stress. It is concluded that combined effect of drought and heat stress was more detrimental than individual stresses. The interactive effect of both stresses was hypo-additive in nature, but for some traits (like turgor potential and APX) effect of one stress neutralized the other. To best of our knowledge, this is the first report on physiological and biochemical response of flag leaf of wheat to combine heat and drought stress. These results will help future studies dealing with improved stress tolerance in wheat. However, detailed studies are needed to fully understand the genetic mechanisms behind these physiological and biochemical changes in flag leaf in response to combined heat and drought stress.
Journal Article