Distance plus attention for binding affinity prediction.

Protein-ligand binding affinity plays a pivotal role in drug development, particularly in identifying potential ligands for target disease-related proteins. Accurate affinity predictions can significantly reduce both the time and cost involved in drug development. However, highly precise affinity prediction remains a research challenge. A key to improve affinity prediction is to capture interactions between proteins and ligands effectively. Existing deep-learning-based computational approaches use 3D grids, 4D tensors, molecular graphs, or proximity-based adjacency matrices, which are either resource-intensive or do not directly represent potential interactions. In this paper, we propose atomic-level distance features and attention mechanisms to capture better specific protein-ligand interactions based on donor-acceptor relations, hydrophobicity, and π -stacking atoms. We argue that distances encompass both short-range direct and long-range indirect interaction effects while attention mechanisms capture levels of interaction effects. On the very well-known CASF-2016 dataset, our proposed method, named Distance plus Attention for Affinity Prediction (DAAP), significantly outperforms existing methods by achieving Correlation Coefficient (R) 0.909, Root Mean Squared Error (RMSE) 0.987, Mean Absolute Error (MAE) 0.745, Standard Deviation (SD) 0.988, and Concordance Index (CI) 0.876. The proposed method also shows substantial improvement, around 2% to 37%, on five other benchmark datasets. The program and data are publicly available on the website https://gitlab.com/mahnewton/daap. Scientific Contribution StatementThis study innovatively introduces distance-based features to predict protein-ligand binding affinity, capitalizing on unique molecular interactions. Furthermore, the incorporation of protein sequence features of specific residues enhances the model's proficiency in capturing intricate binding patterns. The predictive capabilities are further strengthened through the use of a deep learning architecture with attention mechanisms, and an ensemble approach, averaging the outputs of five models, is implemented to ensure robust and reliable predictions.

Correction: 'They eat it like sweets': A mixed methods study of antibiotic perceptions and their use among patients, prescribers and pharmacists in a district hospital in Kabul, Afghanistan.

[This corrects the article DOI: 10.1371/journal.pone.0260096.].

Identification and Validation of Functional miRNAs and Their Main Targets in Sorghum bicolor.

MicroRNAs (miRNAs) are typically non-coding RNAs of 18-26 nucleotides (nts) that are produced endogenously and regulated post-transcriptionally through degradation or translational repression. Since miRNAs are evolutionarily conserved, their preservation is essential for important regulatory functions in plant development, growth, and responses to environmental stress. Sorghum bicolor (sbi) is a valuable food and fodder crop which is grown worldwide. A range of sbi miRNAs were identified so far as being connected to plant development and stress responses. Herein, we employed a variety of bioinformatics tools for miRNA profiling in sbi and a PCR-based platform for the validation of these miRNAs. In total, 74 new conserved sbi miRNAs from 52 miRNA families have been predicted. Using the psRNA Target method, 10613 different protein targets of these predicted miRNAs have been attained. These targets include 54 GO-terms which have substantial targets in the biological, molecular, and cellular processes. We particularly found that the sbi-miR1861c and sbi-miR5050 are involved to regulate sulphur compound biosynthetic process, while the significant spliceosomal complex is regulated by sbi-miR815b and sbi-miR7768b. Also, we report that the pre-ribosome, electron transport chain, cell communication, cellular respiration, protein localization, and photosynthesis are controlled by sbi-miR2907b, sbi-miR530, sbi-miR7749, sbi-miR1858a, sbi-mi7729a, and sbi-miR417, respectively. The identification and validation of these novel sbi miRNAs shall contribute a lot in improving the crop yield and ensure sustainable agriculture.

Assessment and Exposure Analysis of Trace Metals in Different Age Groups of the Male Population in Southern Punjab, Pakistan.

In developing countries, like Pakistan, the pursuit of urbanization and economic development disrupts the delicate ecosystem, resulting in additional biogeochemical emissions of heavy metals into the human habitat and posing significant health risks. The levels of these trace elements in humans remain unknown in areas at higher risk of pollution in Pakistan. In this investigation, selected trace metals including Copper (Cu), Chromium (Cr), Lead (Pb) Cadmium (Cd), Cobalt (Co), Nickel (Ni), and Arsenic (As) were examined in human hair, urine, and nail samples of different age groups from three major cities (Muzaffargarh, Multan, and Vehari) in Punjab province, Pakistan. The results revealed that the mean concentrations (ppm) of Cr (1.1) and Cu (9.1) in hair was highest in Muzaffargarh. In urine samples, the mean concentrations (µg/L) of Co (93), As (79), Cu (69), Cr (56), Ni (49), Cd (45), and Pb (35) were highest in the Multan region, while As (34) and Cr (26) were highest in Vehari. The mean concentrations (ppm) of Ni (9.2), Cr (5.6), and Pb (2.8), in nail samples were highest in Vehari; however, Multan had the highest Cu (28) concentration (ppm). In urine samples, the concentrations of all the studied metals were within permissible limits except for As (34 µg/L) and Cr (26 µg/L) in Vehari. However, in nail samples, the concentrations of Ni in Multan (8.1 ppm), Muzaffargarh (9 ppm), Vehari (9.2 ppm), and Cd (3.69 ppm) in Muzaffargarh exceeded permissible limits. Overall, the concentrations of metals in urine, nail, and hair samples were higher in adults (39-45 age group). Cr, Cu, and Ni revealed significantly higher concentrations of metals in hair and water in Multan, whereas As in water was significantly (p < 0.001) correlated with urinary As in Multan, indicating that the exposure source was region-specific.

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 .

Effect of foliar applied zinc sulphate on phenotypic variability, association and heritability of yield and zinc biofortification related traits of wheat genotypes.

Wheat is an important food crop worldwide, providing substantial calories and nourishment. Genetic variability in wheat germplasm is crucial for the development of cultivars with desirable features. This two years study (2020-21 and 2021-22) was conducted to evaluate 13 diverse wheat genotypes factorially combined with foliar-applied zinc sulphate (0, 0.4, 0.6%) arranged in a triplicate randomized complete block design. Boxplot analysis revealed the significant (P < 0.01) phenotypic variation of wheat germplasm for all the studied traits, but maximum variation was observed for yield and Zn biofortification-related traits. Correlation and path analysis revealed a significant (P < 0.01) association among yield and biofortification-related traits. Zinc uptake showed maximum strength of association (r = 0.96, p < 0.01) with grain Zn concentration. The Biplot analysis showed the graphical representation of wheat accessions based on similar characteristics and then assort into distinct groups. Broadsense heritability (Hbs) was calculated to determine the proportion of variation transmitted to future generations. The high value of Hbs for yield and Zn biofortification-related traits indicates that these traits are governed by the additive type of gene action and can be fixed in early segregating generations. In crux, this study validated the genetic variability in existing wheat genotypes for yield and Zn biofortification-related traits and may be helpful to devise an efficient breeding program for wheat Zn biofortification.

Anthracene-bridged sensitizers for environmentally compatible dye-sensitized solar cells: In silico modelling and prediction.

Advancement in solar cells has gained the attention of researchers due to increasing demand and renewable energy sources. Modeling of electron absorbers and donors has been performed extensively for the development of efficient solar cells. In this regard, efforts are being made for designing effective units for the active layer of solar cells. In this study, CXC22 was utilized as a reference in which acetylenic anthracene acted as a π bridge and infrastructure was D-π-A. We theoretically designed four novel dye-sensitized solar cells JU1-JU4 by utilizing reference molecules to improve the photovoltaic and optoelectronic properties. All designed molecules differ from R by donor moiety modifications. Different approaches were done to R and all molecules to explore different analyses like binding energies, excitation energies, dipole moment, TDM (transition density matrix), PDOS (partial density of states), absorption maxima, and charge transfer analysis. For the evaluation of results, we used the DFT technique and the findings demonstrated that the JU3 molecule showed a better redshift absorption value (761 nm) as compared to all other molecules due to the presence of anthracene in the donor moiety which lengthens the conjugation. JU3 was proven to be the best candidate among all due to improved excitation energy (1.69), low energy band gap (1.93), higher λmax value, and improved electron and hole energy values leading toward higher power conversion efficiency. All the other theoretically formed molecules exhibited comparable outcomes as compared to a reference. As a result, this work revealed the potential of organic dyes with anthracene bridges for indoor optoelectronic applications. These unique systems are effective contributors to the development of high-performance solar cells. Thus, we provided efficient systems to the experimentalists for the future development of solar cells.

Real-to-bin conversion for protein residue distances.

Protein Structure Prediction (PSP) has achieved significant progress lately. Prediction of inter-residue distances by machine learning and their exploitation during the conformational search is largely among the critical factors behind the progress. Real values than bin probabilities could more naturally represent inter-residue distances, while the latter, via spline curves more naturally helps obtain differentiable objective functions than the former. Consequently, PSP methods that exploit predicted binned distances perform better than those that exploit predicted real-valued distances. To leverage the advantage of bin probabilities in getting differentiable objective functions, in this work, we propose techniques to convert real-valued distances into distance bin probabilities. Using standard benchmark proteins, we then show that our real-to-bin converted distances help PSP methods obtain three-dimensional structures with 4%-16% better root mean squared deviation (RMSD), template modeling score (TM-Score), and global distance test (GDT) values than existing similar PSP methods. Our proposed PSP method is named real to bin (R2B) inter-residue distance predictor, and its code is available from https://gitlab.com/mahnewton/r2b.

Ellis-van Creveld syndrome in a neonate: a case report.

Ellis-Van Creveld Syndrome (EVC) is a rare genetic disorder with autosomal recessive inheritance, caused by mutations in two genes, EVC1 and EVC2 in the 4p16 chromosome. The exact prevalence of EVC is unknown and is estimated at approximately seven per million. It affects males and females equally. It is a constellation of four findings, including chondrodysplasia, polydactyly, ectodermal dysplasia, and congenital heart defects. Our case was unique as it had left inguinal hernia, short phallus, hyperpigmented scrotum, cryptorchidism, and other defining features of this syndrome. A multidisciplinary team managed this patient with regular follow up. Only six cases have been reported in Pakistan, and only one of them was reported in a neonate. This report highlights the importance of timely and proper multidisciplinary management of such disorders for better outcomes. It will also create awareness among medical professionals and will help them to identify promptly.

Computational Investigation of Near-Infrared-Absorbing Indeno[1,2-b]indole Analogues as Acceptors in Organic Photovoltaic Devices.

Organic solar cells (OSCs) with fullerene-free acceptors have recently been in high demand in the solar cell market because OSCs are less expensive, more flexible, long-lasting, eco-friendly, and, most importantly, have better photovoltaic performance with a higher PCE. We used INTIC as our reference R molecule and designed five new molecules DF1-DF5 from this R molecule. We attempted to test the power conversion efficiencies of five designed novel molecules, DF1-DF5. Therefore, we compared the PCE values of DF1-DF5 with that of R. We used a variety of computational techniques on these molecules to achieve this goal. Among the designed molecules, DF5 proved to be the best due to its lowest H-L bandgap energy E g (1.82 eV), the highest value of λmax (844.58 nm) within dichloromethane, the lowest excitation energy (1.47 eV), and the lowest oscillator strength value. The newly designed molecule DF2 exhibited the highest dipole moment (21.98 D), while DF3 displayed the minimum binding energy (0.34 eV) and the highest V oc value (1.37 V) with HOMOdonor-LUMOacceptor. According to the partial density of states (PDOS) and transition density matrix (TDM) analysis, DF2 and DF5 exhibited the best results. Charge-transfer (CT) analysis of the blend DF5 and PTB7-Th confirmed the accepting nature of the DF5 molecule. These findings show that by modifying the end-capped units, we can create customized molecules with improved photovoltaic properties. These findings also show that when compared with R, all of the designed molecules DF1-DF5 have improved optoelectronic properties. As a result, it is strongly advised to employ these conceptualized molecules in the practical synthesis of organic solar cells (OSCs).

Impact of Post-Covid-19 on driver behaviour: A perspective towards pandemic-sustained transportation.

Introduction: With the announcement of novel Coronavirus disease 2019 (Covid-19) as a pandemic by World Health Organization (WHO) in March 2020, the whole world went into a lockdown that heavily affected human economic and social life. Since December 2020, with the discovery of effective vaccines, the world is now returning to some normality, particularly for those who are vaccinated. The multimodal transportation has resumed with majority of vaccinated drivers being back on road, driving to their work, and providing transport services. However, there are still several long-term Post-Covid-19 factors, affecting driver health and psychology. Methods: The study deployed a systematic search strategy and selected 62 research publications after rigorous evaluation of the literature. The review was based on (1) forming the inclusion and exclusion criteria, (2) selecting the appropriate keywords, and (3) searching of relevant publications and assessing the eligible articles. Results: A broad perspective study is carried out to gauge the impact of Post-Covid-19 scenarios on the driver physical health and mindset in the context of road safety and pandemic-sustained transportation. It was found that the Post-Covid-19 factors such as wearing face-mask during driving, taking oral anti-viral drugs, and fear of contracting disease, significantly impact the driver's performance and situation awareness skills. The analysis suggested that driver's health vitals and psychological driving awareness can be precisely detected through hybrid driver state monitoring methods. Conclusions: The paper conducts a comprehensive review of the published work and provides unique research opportunities to counteract the challenges involved in precise monitoring of driver behaviour under the effects of different Post-Covid-19 factors. The perspective suggested the possible solutions to live with the pandemic in the context of pandemic-sustained transportation.

Spatial green growth in China: exploring the positive role of investment in the treatment of industrial pollution.

The industrial sector of China is critical to the country's economic growth. On the other side, industrialisation has resulted in a high rate of emissions, pushing China to spend extensively on industrial pollution remediation. As a result, this study looks at the relationship between investment completed in the treatment of industrial pollution and economic development. Initially, the study used the global Moran's I test (Queen's contiguity matrix) to find spatial autocorrelation for the 'investment completed in the treatment of industrial pollution' factor, where the study found a positive association across Chinese provinces, and suggest the existence of spatial autocorrelation. Thereafter, a time-fixed effect spatial error model was used due to the lowest Akaike information criterion and Bayesian information criterion to analyse regional data of China from 1999 to 2018. The data reveal a positive association between investment completed in the treatment of industrial pollution and regional economic growth, both in the short and long term. Furthermore, the negative consequences of urban wages and foreign investment on investment completed in the treatment of industrial pollution are having the reverse effect on regional green development, necessitating ecologically friendly actions to mitigate the negative environmental effects of both. The results highlight the need for policymakers in other countries to review their plans for economic expansion and create environmentally friendly legislation. By implementing the Chinese green economic growth model, policymakers in industrially polluting nations can reduce industrial pollution and foster green growth in their nation.

Interactive effect of elevated CO2 and drought on physiological traits of Datura stramonium.

Rising atmospheric CO2 concentrations are known to influence the response of many plants under drought. This paper aimed to measure the leaf gas exchange, water use efficiency, carboxylation efficiency, and photosystem II (PS II) activity of Datura stramonium under progressive drought conditions, along with ambient conditions of 400 ppm (aCO2) and elevated conditions of 700 ppm (eCO2). Plants of D. stramonium were grown at 400 ppm and 700 ppm under 100 and 60% field capacity in a laboratory growth chamber. For 10 days at two-day intervals, photosynthesis rate, stomatal conductance, transpiration rate, intercellular CO2 concentration, water use efficiency, intrinsic water use efficiency, instantaneous carboxylation efficiency, PSII activity, electron transport rate, and photochemical quenching were measured. While drought stress had generally negative effects on the aforementioned physiological traits of D. stramonium, it was found that eCO2 concentration mitigated the adverse effects of drought and most of the physiological parameters were sustained with increasing drought duration when compared to that with aCO2. D. stramonium, which was grown under drought conditions, was re-watered on day 8 and indicated a partial recovery in all the parameters except maximum fluorescence, with this recovery being higher with eCO2 compared to aCO2. These results suggest that elevated CO2 mitigates the adverse growth effects of drought, thereby enhancing the adaptive mechanism of this weed by improving its water use efficiency. It is concluded that this weed has the potential to take advantage of climate change by increasing its competitiveness with other plants in drought-prone areas, suggesting that it could expand into new localities.

Exploring the association between agricultural production systems and household diets in Viet Nam.

The government of Viet Nam promotes an integrated and diversified production system that focuses on the symbiotic relationship of livestock, aquaculture, and fruits and vegetables (F&V), locally known as Vuon Ao Chuong (VAC). The expectation is that this system can prevent soil degradation, while improving dietary quality and income. This study examines the correlation between VAC production systems and diets using cross-sectional data from the 2016 round of the Viet Nam Household Living Standards Survey (VHLSS). Using ordinary least squares, we model four continuous outcome variables related to quantity consumed of fruits and vegetables, fiber, animal protein, and dietary energy; while using logistical regression, we model three indicator variables related to whether diets are balanced in terms of intake of dietary energy derived from carbohydrates, proteins, and fats. While individual components of VAC, such as aquaculture or F&V production, show a positive correlation with one or more dietary indicators, adoption of the full VAC system is found to be positively correlated only with dietary fiber consumption, making it challenging to establish a causal link between system adoption and improved dietary quality. However, we find that several socioeconomic variables, such as access to markets, household wealth, education of the household members, and household size are positively associated with one or more dietary indicators. Further research is needed to establish strong and causal relationships, or lack thereof, between VAC system and diets by exploiting the panel structure of VHLSS to examine the role of VAC in improving nutritional outcomes in Viet Nam.

Constraint Guided Beta-Sheet Refinement for Protein Structure Prediction.

Protein structure prediction (PSP) is a crucial issue in Bioinformatics. PSP has its important use in many vital research areas that include drug discovery. One of the important intermediate steps in PSP is predicting a protein's beta-sheet structures. Because of non-local interactions among numerous irregular areas in beta-sheets, their highly accurate prediction is challenging. The challenge is compounded when a given protein's structure has a large number of beta-sheets. In this paper, we specifically refine the beta-sheets of a protein structure by using a local search method. Then, we use another local search method to refine the full structure. Our search methods analyse residue-residue distance-based scores and apply geometric restrictions gained from deep learning models. Moreover, our search methods recognise the regions of the current conformations prompting the nether scores and generate neighbouring conformations focusing on that identified regions and making alterations there. On a set of standard 88 proteins of various sizes between 46 and 450 residues, our method successfully outperforms state-of-the-art PSP search algorithms. The improvements are more than 12% in average root mean squared distance (RMSD), template modelling score (TM-score), and global distance test (GDT) values.

A novel chest CT severity score in COVID-19 and its correlation with severity and prognosis of the lung disease: A retrospective cohort study.

Background: HRCT chest has a high sensitivity in the diagnosis of patients with COVID-19 infection. Through our study, we intend to evaluate the diagnostic accuracy and inter-reader variability of a semi-quantitative CT severity score, a novel parameter designed for risk stratification and prognostication of COVID-19 pneumonia with clinical staging of disease. Methods: It was a single-center retrospective analysis performed on an original cohort of 4180 symptomatic patients with the suspicion of SARS-CoV-2 interstitial pneumonia. Out of 4180, a total of 4004 patients with COVID-19 were confirmed by an RT-PCR. We used an HRCT chest severity score (CT-SS) to evaluate the COVID-19 disease burden on the initial scan obtained at admission. The data were analyzed with IBM SPSS Statistics Version 22.0 Release 2013. Results: Our study subjects demonstrated the most common clinical features fever, cough, dyspnea, and body aches. Raised CRP levels (CRP >0.5 mg/dL) were found in 81.86% and increased D-dimer levels (>500 ng/mL) were found in 92.3% of patients. The most common radiological findings of the disease included ground-glass opacities, observed in 98.8%. Our study has a sensitivity of 89.2%, a specificity of 94.8%, a positive predictive value (PPV) of 90.6%, and a negative predictive value (NPV) of 94%. Conclusion: As per our findings, this novel CT scoring system might aid in the risk stratification and the short-term prognostication of patients suffering from COVID-19 pneumonia. This will eventually help in curtailing the extensive burden on the healthcare system amid the current pandemic.

Mercury fractionation, bioavailability, and the major factors predicting its transfer and accumulation in soil-wheat systems.

Soil mercury (Hg) and its bioaccumulation in food crops have attracted widespread concerns globally due to its harmful effects on biota. However, soil mercury fractionation, bioavailability, and the major factors predicting its transfer and accumulation in soil-wheat-systems have not been thoroughly explored. Twenty-one (21) soil samples collected throughout China with a wide spectrum of physico-chemical characteristics were contaminated with HgCl2 and winter wheat (Triticum aestivum L.) was grown on the soils in a greenhouse pot-culture experiment for 180 days. A four-step sequential extraction was used segregating soil Hg into water-soluble (F1, 0.21 %), exchangeable (F2, 0.07 %), organically bound (F3, 16.40 %), and residual fractions (F4, 83.32 %). Step-wise multiple linear regression (SMLR) and path analysis (PA) were used to develop a prediction model and identify the major controlling factors of soil-wheat Hg transference. The SMLR results revealed that wheat Hg in leaves, husk, and grain was positively correlated with soil total and available Hg, and crystalline manganese (Cryst-Mn), while negatively correlated with soil pH, amorphous manganese (Amor-Mn) and crystalline aluminium (Cryst-Al). Bioconcentration factor (BCF) values were significantly higher in acidic soils (highest 0.05), with phytotoxic effects in some soils, as compared to alkaline soils (lowest 0.006). Furthermore, wheat grain Hg was significantly correlated with total (R2 = 0.25), water-soluble (R2 = 0.54) and NH4Ac-extractable Hg (R2 = 0.43) while also had a good correlation with soil pH (R2 = -0.20). In conclusion, the soil total and available Hg (water-soluble + exchangeable fraction), pH, organic matter, and Amor-Mn are the most important soil variables that support Hg uptake in the wheat plants, which benefit managing Hg-enriched agricultural soils for safe wheat production.

A stacked meta-ensemble for protein inter-residue distance prediction.

Predicted inter-residue distances are a key behind recent success in high quality protein structure prediction (PSP). However, prediction of both short and long distance values together is challenging. Consequently, predicted short distances are mostly used by existing PSP methods. In this paper, we use a stacked meta-ensemble method to combine deep learning models trained for different ranges of real-valued distances. On five benchmark sets of proteins, our proposed inter-residue distance prediction method improves mean Local Distance Different Test (LDDT) scores at least by 5% over existing such methods. Moreover, using a real-valued distance based conformational search algorithm, we also show that predicted long distances help obtain significantly better protein conformations than when only predicted short distances are used. Our method is named meta-ensemble for distance prediction (MDP) and its program is available from https://gitlab.com/mahnewton/mdp.

Enhancing protein contact map prediction accuracy via ensembles of inter-residue distance predictors.

Protein contact maps capture coevolutionary interactions between amino acid residue pairs that are spatially within certain proximity threshold. Predicted contact maps are used in many protein related problems that include drug design, protein design, protein function prediction, and protein structure prediction. Contact map prediction has achieved significant progress lately but still further challenges remain with prediction of contacts between residues that are separated in the amino acid residue sequence by large numbers of other residues. In this paper, with experimental results on 5 standard benchmark datasets that include membrane proteins, we show that contact map prediction could be significantly enhanced by using ensembles of various state-of-the-art short distance predictors and then by converting predicted distances into contact probabilities. Our program along with its data is available from https://gitlab.com/mahnewton/ecp.

Foliar application of mepiquat chloride and nitrogen improves yield and fiber quality traits of cotton (Gossypium hirsutum L.).

Cotton (Gossypium hirsutum L.) is one of the most important cash crops primarily grown for fiber. It is a perennial crop with indeterminate growth pattern. Nitrogen (N) is extremely important for vegetative growth as balanced N-nutrition improves photosynthesis, resulting in better vegetative growth. Excessive N-supply results in more vegetative growth, which increases the incidence of insect pest and diseases' infestation, pollute surface and ground water, delays maturity and produces low crop yield with poor quality. The use of plant growth regulators (PGRs) is an emerging option to control excessive vegetative growth. The PGRs help in improving plant architecture, boll retention, boll opening, yield and quality by altering growth and physiological processes such as photosynthesis, assimilate partitioning and nutrients dynamic inside the plant body. Mepiquat chloride (1,1-dimethylpiperidinum chloride) is globally used PGR for canopy development and control of excessive vegetative growth in cotton. This study investigated the effect of mepiquat chloride (MC) and N application on yield and yield components of transgenic cotton variety 'BT-FSH-326'. Two N rates (0, 198 kg ha-1) and five MC rates (0, 30,60, 90 and 120 g ha-1) were included in the study. Results revealed that MC and N application improved boll weight, number of bolls per plant, and seed cotton and lint yields. The highest seed cotton and lint yields (3595 kg ha-1 and 1701 kg ha-1, respectively) were observed under foliar application of 198 kg ha-1 N and 120 g ha-1 MC. Fiber length, fiber strength, micronaire and uniformity were significantly improved with foliar application of MC and N. In conclusion, foliar application of MC and N could be helpful in improving yield and fiber quality of cotton.