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Introduction Phytic acid (PA) is an important antinutrient agent present in cereal grains which reduces the bioavailability of iron and zinc in human body, causing malnutrition. Inositol pentakisphosphate 2- kinase 1 (IPK1) gene has been reported to be an important gene for PA biosynthesis. Objective A recent genome editing tool CRISPR/Cas9 has been successfully applied to develop biofortified rice by disrupting IPK1 gene, however, it remained a challenge in wheat. The aim of this study was to biofortify wheat using CRISPR/Cas9. Methodology In this study, we isolated 3 TaIPK1 homeologs in wheat designated as TaIPK1.A, TaIPK1.B and TaIPK1.D and found that the expression abundance of TaIPK1.A was stronger in early stages of grain filling. Using CRISPR/Cas9, we have disrupted TaIPK1.A gene in cv. Borlaug-2016 with two guide RNAs targeting the 1st and 2nd exons. Results We got several genome-edited lines in the T0 generation at frequencies of 12.7% and 10.8%. Sequencing analysis revealed deletion of 1–23 nucleotides and even an addition of 1 nucleotide in various lines. Analysis of the genome-edited lines revealed a significant decrease in the PA content and an increase in iron and zinc accumulation in grains compared with control plants. Conclusion Our study demonstrates the potential application of CRISPR/Cas9 technique for the rapid generation of biofortified wheat cultivars.

Single-step, single-precursor synthesis of nitrogen-doped graphene oxide (N-GO) was demonstrated in this work. By choosing aniline as the sole source of carbon and nitrogen, N-GO films were fabricated using microwave plasma at a power as low as 80 W in atmospheric conditions. The aniline vapor dissociated under plasma formed islands of N-GO nanosheets on the substrates or walls of the quartz deposition chamber. The interplanar spacing in the pristine N-GO films was observed to be lower than that of GO films, which indicated a lower concentration of oxygen and other species present in the space between the N-GO layers. The as-fabricated N-GO demonstrated superior antiscaling and algicidal properties that are deemed imperative for water purification applications.

Annually, the thermal coal industries produce billion tons of fly-ash (FA) as a waste by-product. Which has been proficiently used for the manufacture of FA based geopolymer concrete (FGC). To accelerate the usage of FA in building industry, an innovative machine learning techniques namely gene expression programming (GEP) and multi expression programming (MEP) are employed for forecasting the compressive strength of FGC. The comprehensive database is constructed comprising of 311 compressive strength results. The obtained equations relate the compressive strength of FGC with eight most effective parameters i.e., curing regime (T), time for curing (t) in hours, age of samples (A) in days, percentage of total aggregate by volume (% Ag), molarity of sodium hydroxide (NaOH) solution (M), silica (SiO2) solids percentage in sodium silicate (Na2SiO3) solution (%S), superplasticizer (%P) and extra water (%EW) as percent FA. The accurateness and predictive capacity of both GEP and MEP model is assessed via statistical checks, external validation criteria suggested by different researcher and then compared with linear regression (LR) and non-linear regression (NLR) models. In comparison with MEP equation, the GEP equation has lesser statistical error and higher correlation coefficient. Also, the GEP equation is short and it would be easy to use in the field. So, the GEP model is further utilized for sensitivity and parametric study. This research will increase the re-usage of hazardous FA in the development of green concrete that would leads to environmental safety and monetarist reliefs.

Graphene is a highly sought-after material for a wide range of applications, particularly in areas such as energy harvesting and storage, electronics, electrochemical sensors, biomedical, composites, and coatings. The synthesis of high-quality graphene is a precondition for its real-time application. However, conventional synthesis methods have certain drawbacks including laborious procedures and structural defects in graphene nanosheets. Plasma-based synthesis techniques such as plasma-enhanced chemical vapor deposition and atmospheric pressure microwave plasma are high-tech synthesis practices that can produce graphene without any solvents in a few seconds. This article reviews these state-of-the-art techniques emphasizing mainly their process parameters for the synthesis of high-grade graphene, which is defect-free, and comprises mono to few layers, great carrier mobility, and high purity. The applications of as-synthesized graphene in various fields are also provided. Moreover, the potential breakthroughs and the prospect of these techniques are also discussed in this work.

To identify the bacterial agent responsible for the surge of cases of acute diarrhoeal disease in Karachi, Pakistan, and assess the antimicrobial susceptibility pattern of the isolates.Observational study.Department of Microbiology, Indus HospitalHealth Network, Karachi, Pakistan, from 1st March to 31st May 2022.Vibrio cholerae (V. Cholerae) isolates from stool samples received in the laboratory in the months of March, April, and May were identified and antibiotic susceptibility testing was performed by the Kirby-Bauer disk diffusion method. Serology was performed to identify the serotype causing a surge in cases of acute watery diarrhoea.A total of three-hundred and seventy-eight stool samples were received during the study period, out of which seventy-eight were positive for V. cholerae serogroup O1 serotype Ogawa. The isolate tested 100% sensitive for Ciprofloxacin, Tetracycline, Doxycycline, and Azithromycin, while 74% resistance was observed in Trimethoprim/Sulfamethoxazole and 25% resistance in Ampicillin.V. cholerae serogroup O1 serotype Ogawa was the causative agent responsible for cholera outbreak in Karachi during the early summer of 2022. Ciprofloxacin, Tetracycline, Doxycycline, and Azithromycin are appropriate antimicrobials for treatment; whereas resistance to Trimethoprim/Sulfamethoxazole, and Ampicillin was higher.Vibrio cholerae, Outbreak, Acute diarrhoeal disease.

Leaf morphology influences photosynthesis, transpiration, and ultimately crop yield. However, the molecular mechanism of leaf development is still not fully understood. Here, we identified and characterized the narrow leaf21 (nal21) mutant in rice (Oryza sativa), showing a significant reduction in leaf width, leaf length and plant height, and increased tiller number. Microscopic observation revealed defects in the vascular system and reduced epidermal cell size and number in the nal21 leaf blade. Map-based cloning revealed that NAL21 encodes a ribosomal small subunit protein RPS3A. Ribosome-targeting antibiotics resistance assay and ribosome profiling showed a significant reduction in the free 40S ribosome subunit in the nal21 mutant. The nal21 mutant showed aberrant auxin responses in which multiple auxin response factors (ARFs) harboring upstream open-reading frames (uORFs) in their 5′-untranslated region were repressed at the translational level. The WUSCHEL-related homeobox 3A (OsWOX3A) gene, a key transcription factor involved in leaf blade lateral outgrowth, is also under the translational regulation by RPS3A. Transformation with modified OsARF11, OsARF16, and OsWOX3A genomic DNA (gDNA) lacking uORFs rescued the narrow leaf phenotype of nal21 to a better extent than transformation with their native gDNA, implying that RPS3A could regulate translation of ARFs and WOX3A through uORFs. Our results demonstrate that proper translational regulation of key factors involved in leaf development is essential to maintain normal leaf morphology.

The prediction of the earthquake has been a testing investigation field, where a prediction of the impending incidence of destructive calamity is made. In this research, eight seismic features are processed by utilizing seismological notions, such as seismic quiescence, the eminent geophysical specifics of Gutenberg–Richter’s inverse law, and dissemination of typical earthquake extents for earthquake prediction. A classification system based on support vector regressor (SVR) along with hybrid neural network (HNN) is formed to attain the predictions of earthquakes for the Hindukush region. The challenge is expressed as a binary classification undertaking, and for earthquakes of magnitude equal to or more than 5.5, the predictions are generated for 1 month. HNN is a step-by-step amalgamation of three diverse neural networks, and enhanced particle swarm optimization (EPSO) is used to extend weight optimization at an individual layer, thus enhancing the performance of HNN. In amalgamation with the SVR-HNN prediction system, the freshly processed seismic aspects are applied to the Hindukush region. For analyzing the results, another considered performance measure is accuracy. Comparative to earlier prediction investigations, the achieved numerical outcomes demonstrate enhanced prediction implementation for the considered region.

High temperature electrical properties of morphotropic phase boundary BiScO3-PbTiO3 compositions, exhibiting high piezoelectric coefficient, saturation polarization, field induced strain and low dielectric loss have been presented in detail. Dielectric loss and various electrical parameters have been comprehensively utilized to identify multiple dipolar relaxation dynamics in the regime where conductivity effects are low. Dielectric data confirms presence of various polymorphs exhibiting lower (higher) symmetry configuration, which extends to wider (narrower) temperature regime. Effect of conductivity associated with the charge transport is not dominant up to 600 K, which shows that this material is a promising candidate for high temperature applications.

The purpose of this article is to demonstrate the potential of gene expression programming (GEP) in anticipating the compressive strength of circular CFRP confined concrete columns. A new GEP model has been developed based on a credible and extensive database of 828 data points to date. Numerous analyses were carried out to evaluate and validate the presented model by comparing them with those presented previously by different researchers along with external validation comparison. In comparison to other artificial intelligence (AI) techniques, such as Artificial Neural Networks (ANN) and the adaptive neuro-fuzzy interface system (ANFIS), only GEP has the capability and robustness to provide output in the form of a simple mathematical relationship that is easy to use. The developed GEP model is also compared with linear and nonlinear regression models to evaluate the performance. Afterwards, a detailed parametric and sensitivity analysis confirms the generalized nature of the newly established model. Sensitivity analysis results indicate the performance of the model by evaluating the relative contribution of explanatory variables involved in development. Moreover, the Taylor diagram is also established to visualize how the proposed model outperformed other existing models in terms of accuracy, efficiency, and being closer to the target. Lastly, the criteria of external validation were also fulfilled by the GEP model much better than other conventional models. These findings show that the presented model effectively forecasts the confined strength of circular concrete columns significantly better than the previously established conventional regression-based models.

Traditional object detection answers two questions; “what” (what the object is?) and “where” (where the object is?). “what” part of the object detection can be fine grained further i-e. “what type”, “what shape” and “what material” etc. This results in shifting of object detection task to object description paradigm. Describing object provides additional detail that enables us to understand the characteristics and attributes of the object (“plastic boat” not just boat, “glass bottle” not just bottle). This additional information can implicitly be used to gain insight about unseen objects (e.g. unknown object is “metallic”, “has wheels”), which is not possible in traditional object detection. In this paper, we present a new approach to simultaneously detect objects and infer their attributes, we call it Detectand- Describe (DaD) framework. DaD is a deep learning-based approach that extends object detection to object attribute prediction as well. We train our model on aPascal train set and evaluate our approach on aPascal test set. We achieve 97.0% in Area Under the Receiver Operating Characteristic Curve (AUC) for object attributes prediction on aPascal test set. We also show qualitative results for object attribute prediction on unseen objects, which demonstrate the effectiveness of our approach for describing unknown objects.

Landslides are one form of geohazard that is crucial for risk assessment and mitigation. This study proposes machine learning methods to identify landslides using data collected from Muzaffarabad, Pakistan. We applied classification techniques including Random Forest, Decision Tree, Gradient Boosting Machine (GBM), light gradient boosting machine (LGBM), neural network (NN), 1D-CNN (convolutional neural network one dimensional), XGBoost, and Catboost. Our dataset consists of 12 different factors and data of 1213 sites to predict a landslide. Initially, we trained models individually and compared their performances to find a model with the highest accuracy. In the end, multiple performance measures are used for the evaluation of applied models. From the results, we achieved the highest test accuracy of 84% using NN and boosting methods, with more than 80% accuracy. Our proposed system can be effectively used to predict landslides for other sensitive regions with comparable environmental conditions.

Graphene nanostructures exhibit a wide range of remarkable properties suitable for many applications, including those in the field of biomedical engineering. In this work, plasma-enhanced chemical vapor deposition was utilized at different applied RF power for the fabrication of vertical graphene nanowalls on silicon and quartz substrates from an inherently volatile carbon precursor without the use of any catalyst. AFM confirmed the presence of very sharp exposed graphene edges, with associated high surface roughness. The hydrophobicity of the material increased with the power of deposition, reaching the water contact angle of 123 ˚ for 500 W. Confocal scanning laser microscopy demonstrated that the viability of gram-negative Escherichia coli and gram-positive Staphylococcus aureus cells were 33% and 37% when incubated on graphene samples, respectively, compared to controls (quartz) that showed the viability of 82% and 84%, respectively. SEM verified significant morphological damage to bacterial cell walls by the sharp edges of graphene walls, with cells appearing abnormal and deformed. The presented data clearly contributed to the current understanding of the mechanical-bactericidal mechanism of vertically oriented graphene nanowalls upon direct contact with microorganisms.

Optimum soil water availability is vital for maximum yield production in rice which is challenged by increasing spells of drought. The reproductive stage drought is among the main limiting factors leading to the drastic reduction in grain yield. The objective of this study was to investigate the molecular and morphophysiological responses of pre-anthesis stage drought stress in green super rice. The study assessed the performance of 26 rice lines under irrigated and drought conditions. Irrigated treatment was allowed to grow normally, while drought stress was imposed for 30 days at the pre-anthesis stage. Three important physiological traits including pollen fertility percentage (PFP), cell membrane stability (CMS), and normalized difference vegetative index (NDVI) were recorded at anthesis stage during the last week of drought stress. Agronomic traits of economic importance including grain yield were recorded at maturity stage. The analysis of variance demonstrated significant variation among the genotypes for most of the studied traits. Correlation and principal component analyses demonstrated highly significant associations of particular agronomic traits with grain yield, and genetic diversity among genotypes, respectively. Our study demonstrated a higher drought tolerance potential of GSR lines compared with local cultivars, mainly by higher pollen viability, plant biomass, CMS, and harvest index under drought. In addition, the molecular basis of drought tolerance in GSR lines was related to upregulation of certain drought-responsive genes including OsSADRI, OsDSM1, OsDT11, but not the DREB genes. Our study identified novel drought-responsive genes (LOC_Os11g36190, LOC_Os12g04500, LOC_Os12g26290, and LOC_Os02g11960) that could be further characterized using reverse genetics to be utilized in molecular breeding for drought tolerance.

Using variable temperature x-ray total scattering in magnetic field, we study the interaction between lattice and magnetic degrees of freedom in MnAs, which loses its ferromagnetic order and hexagonal (‘H’) lattice symmetry at 318 K to recover the latter and become a true paramagnet when the temperature is increased to 400 K. Our results show that the 318 K transition is accompanied by highly anisotropic displacements of Mn atoms that appear as a lattice degree of freedom bridging the ‘H’ and orthorhombic phases of MnAs. This is a rare example of a lowering of an average crystal symmetry due to an increased displacive disorder emerging on heating. Our results also show that magnetic and lattice degrees of freedom appear coupled but not necessarily equivalent control variables for triggering phase transitions in strongly correlated systems in general and in particular in MnAs.

Novel vaccination strategies are crucial to efficiently control tuberculosis, as proposed by the World Health Organization under its flagship program "End TB Strategy." However, the emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb), particularly in those coinfected with HIV-AIDS, constitutes a major impediment to achieving this goal. We report here a novel vaccination strategy that involves synthesizing a formulation of an immunodominant peptide derived from the Acr1 protein of Mtb. This nanoformulation in addition displayed on the surface a toll-like receptor-2 ligand to offer to target dendritic cells (DCs). Our results showed an efficient uptake of such a concoction by DCs in a predominantly toll-like receptor-2-dependent pathway. These DCs produced elevated levels of nitric oxide, proinflammatory cytokines interleukin-6, interleukin-12, and tumor necrosis factor-α, and upregulated the surface expression of major histocompatibility complex class II molecules as well as costimulatory molecules such as CD80 and CD86. Animals injected with such a vaccine mounted a significantly higher response of effector and memory Th1 cells and Th17 cells. Furthermore, we noticed a reduction in the bacterial load in the lungs of animals challenged with aerosolized live Mtb. Therefore, our findings indicated that the described vaccine triggered protective anti-Mtb immunity to control the tuberculosis infection.

Various kinds of abiotic stresses such as drought, salinity, extreme heat and cold, and heavy metal contamination in soils significantly have impact on crop production. Due to global climate change, the severity of these stresses is increased. Therefore, development of new abiotic stress-tolerant crop variety is needed for feeding the ever-increasing global population. Although classical breeding techniques have been useful for the development of high yielding varieties for the green revolution in the last seven decades, these time-consuming techniques seem unable to meet the growing challenges of abiotic stresses to crop production. The clustered regularly interspaced short palindromic repeats (CRISPR)-associated enzyme (Cas) technology has recently been emerged as a revolutionary technique for precise editing of the targeted gene(s) in the genome of any organisms including plants. This technology is user-friendly, rapid, and cost-effective. Genomics and postgenomics analyses revealed a large body of knowledge on genes involved in abiotic stress tolerance in plants. Targeted editing (addition/deletion/rearrangement) of these genes associated with plants tolerance to particular abiotic stress is found useful for the improvement of plants for abiotic stress tolerance. However, to make a new green revolution using CRISPR-Cas technology, further improvement of this technology and necessary changes in the global regulatory system for greater acceptance of genome-edited crop plants are needed. This chapter reviews and updates the concept, mechanism, and application of CRISPR-Cas genome editing technology on the improvement of crop plants for abiotic stress tolerance. Furthermore, the future potentials and challenges of this technology in the improvement of crop plants for the tolerance of various abiotic stresses will also be discussed.

To avoid time-consuming, costly, and laborious experimental tests that require skilled personnel, an effort has been made to formulate the depth of wear of fly-ash concrete using a comparative study of machine learning techniques, namely random forest regression (RFR) and gene expression programming (GEP). A widespread database comprising 216 experimental records was constructed from available research. The database includes depth of wear as a response parameter and nine different explanatory variables, i.e., cement content, fly ash, water content, fine and coarse aggregate, plasticizer, air-entraining agent, age of concrete, and time of testing. The performance of the models was judged via statistical metrics. The GEP model gives better performance with R2 and ρ equals 0.9667 and 0.0501 respectively and meet with the external validation criterion suggested in the previous literature. The k-fold cross-validation also verifies the accurateness of the model by evaluating R2, RSE, MAE, and RMSE. The sensitivity analysis of GEP equation indicated that the time of testing is the influential parameter. The results of this research can help the designers, practitioners, and researchers to quickly estimate the depth of wear of fly-ash concrete thus shortening its ecological susceptibilities that push to sustainable and faster construction from the viewpoint of environmentally friendly waste management.

Optimum soil water availability is vital for maximum yield production in rice which is challenged by increasing spells of drought. The reproductive stage drought is among the main limiting factors leading to the drastic reduction in grain yield. Objective of this study was to investigate the molecular and morpho-physiological responses of pre-anthesis stage drought stress in green super rice. The study assessed the performance of 26 rice lines under irrigated and drought conditions. Irrigated treatment was allowed to grow normally while drought stress was imposed for 30 days at pre-anthesis stage. Three important physiological traits including pollen fertility percentage (PFP), cell membrane stability (CMS) and normalized difference vegetative index (NDVI) were recorded at anthesis stage during the last week of drought stress. Agronomic traits of economic importance including grain yield were recorded at maturity stage. The analysis of variance demonstrated significant variation among the genotypes for most of the studied traits. Correlation and principal component analyses demonstrated highly significant associations of particular agronomic traits with grain yield, and genetic diversity among genotypes, respectively. Our study demonstrated a higher drought tolerance potential of GSR lines compared to local cultivars, mainly by higher pollen viability, plant biomass, CMS, and harvest index under drought. In addition, the molecular basis of drought tolerance in GSR lines was related to upregulation of certain drought responsive genes including OsSADRI, OsDSM1, and OsDT11. Our study identified novel drought tolerance related genes (OsDRG-1, OsDRG-2, OsDRG-3 and OsDRG-4) that could be further characterized using reverse genetics to be utilized in molecular breeding for drought tolerance.

This study provides the application of a machine learning-based algorithm approach names “Multi Expression Programming” (MEP) to forecast the compressive strength of carbon fiber-reinforced polymer (CFRP) confined concrete. The suggested computational Multiphysics model is based on previously reported experimental results. However, critical parameters comprise both the geometrical and mechanical properties, including the height and diameter of the specimen, the modulus of elasticity of CFRP, unconfined strength of concrete, and CFRP overall layer thickness. A detailed statistical analysis is done to evaluate the model performance. Then the validation of the soft computational model is made by drawing a comparison with experimental results and other external validation criteria. Moreover, the results and predictions of the presented soft computing model are verified by incorporating a parametric analysis, and the reliability of the model is compared with available models in the literature by an experimental versus theoretical comparison. Based on the findings, the valuation and performance of the proposed model is assessed with other strength models provided in the literature using the collated database. Thus the proposed model outperformed other existing models in term of accuracy and predictability. Both parametric and statistical analysis demonstrate that the proposed model is well trained to efficiently forecast strength of CFRP wrapped structural members. The presented study will promote its utilization in rehabilitation and retrofitting and contribute towards sustainable construction material.

The growth of fake news in multiple fields such as in the political or health sector has become a great concern as it possess huge impact on the reader's mind. Identifying the fake news or differentiating between fake and authentic news is quite challenging. The focus of this research is to identify fake news by applying different artificial intelligence techniques along with different embeddings and to assess the performance of all the applied models. The performance of these models and the embeddings is compared based on precision, accuracy, Fl-score and recall. For machine learning techniques SVM, KNN, Naive Bayes, Logistic Regression and Decision Trees are used, while for deep learning techniques CNN and LSTM are used with GloVe and BERT embeddings. Multiple experiments using these techniques are performed on the LIAR and Fake-or-Real dataset. Naive Bayes has shown the best results from machine learning techniques on both datasets. While in deep learning techniques, LSTM with GloVe has shown the best results on the LIAR dataset and CNN with BERT has shown the best performance on the Fake-or-Real dataset. Overall GloVe word embeddings performed well on the LIAR dataset while BERT sentence embeddings have shown good performance on the Fake-or-Real dataset.

Background The current standard of care in locally advanced rectal cancer (LARC) is neoadjuvant long-course chemoradiotherapy (nCRT) followed by total mesorectal excision (TME). Surgery is conventionally performed approximately 6–8 weeks after nCRT. This study aimed to determine the effect on outcomes of extending this interval. Methods A systematic search was performed for studies reporting oncological results that compared the classical interval (less than 8 weeks) from the end of nCRT to TME with a minimum 8-week interval in patients with LARC. The primary endpoint was the rate of pathological complete response (pCR). Secondary endpoints were recurrence-free survival, local recurrence and distant metastasis rates, R0 resection rates, completeness of TME, margin positivity, sphincter preservation, stoma formation, anastomotic leak and other complications. A meta-analysis was performed using the Mantel–Haenszel method. Results Twenty-six publications, including four RCTs, with 25 445 patients were identified. A minimum 8-week interval was associated with increased odds of pCR (odds ratio (OR) 1·41, 95 per cent c.i. 1·30 to 1·52; P < 0·001) and tumour downstaging (OR 1·18, 1·05 to 1·32; P = 0·004). R0 resection rates, TME completeness, lymph node yield, sphincter preservation, stoma formation and complication rates were similar between the two groups. The increased rate of pCR translated to reduced distant metastasis (OR 0·71, 0·54 to 0·93; P = 0·01) and overall recurrence (OR 0·76, 0·58 to 0·98; P = 0·04), but not local recurrence (OR 0·83, 0·49 to 1·42; P = 0·50). Conclusion A minimum 8-week interval from the end of nCRT to TME increases pCR and downstaging rates, and improves recurrence-free survival without compromising surgical morbidity.