Model Agnostic Meta-Learning(MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating demand.The presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease management.With advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a challenge.This challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease pathogens.In this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data scarcity.This study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural Networks.This paper also proposes a Model AgnosticMeta Learning(MAML)based ensemble classifier for accurate classification.The proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens detection.In future,more diseases can be included for detection along with some other modalities like pests and weed.

A chemical odyssey:Exploring renal stone diversity by age and sex in Punjab,Pakistan

Dear Editor,Renal calculosis is one of the most common urological disorders worldwide,with a prevalence ranging from 1%to 13%across different regions[1].Renal stones are crystal concretions that form on the inner surface of the kidney,resulting from disruptions in the metabolism,the excretion of stone constituents,or the formation of Randall's plaques and plugs.These stones are a result of various endogenous factors.

Evaluating the Potential of Nitrofurantoin and Fosfomycin for E.coli UTIs: A Susceptibility Study

This study was designed to find the susceptibility of Nitrofurantoin and Fosfomycin among urinary isolates of Escherichia.coli.Four hundred(400)urine samples were collected for susceptibility of nitrofurantoin and fosfomycin among urinary isolates of E.coli.All indoor and outdoor patients'urinary samples yielded growth of E.coli.Mid-stream urine specimens were inoculated on blood agar and CLED agar and incubated at 35±2°C.Growth was observed,and Escherichia coli was identified by Gram staining,Catalase,Motility test and API 20E(Bio murex)as per standard procedure.Antimicrobial susceptibility testing of isolates for nitrofurantoin and fosfomycin was carried out by the modified Kirby-Bauer disc diffusion method according to CLSI guidelines ATCC 25922.E.coli was used as a quality control strain.A total of 400 samples were tested susceptibility of nitrofurantoin and fosfomycin among urinary isolates of E.coli during this period.A total of 400 samples yielded the growth of E.coli,out of which 178(44.5%)were male and 222(55.5%)were female samples.Among males,18(10%)were tolerant to nitrofurantoin,and 2(1.1%)were tolerant to fosfomycin.Among females,9(4.09%)were susceptible to nitrofurantoin while 6(2.72%)were susceptible to fosfomycin.Among age groups below 45 years old,6(4.76%)were tolerant to nitrofurantoin,and 2(1.58%)were sensitive to fosfomycin.Between 46-66 years old,4(2.81%)were sensitive to nitrofurantoin,and 3(2.11%)were sensitive to fosfomycin.Between 67-90 years old,17(12.87%)were sensitive to nitrofurantoin,and 4(3.03%)were tolerant to fosfomycin.Fosfomycin and nitrofurantoin showed good susceptibility in urinary isolates of E.coli and can be used empirically in our setup.

Antibody Pattern: Correlation of Age and Gender in ANA and Anti-ds-DNA Prevalence

The study was designed to find the prevalence of ANA antibodies and anti-dsDNA antibodies in samples tested at AFIP Rawalpindi and their correlation with age and gender and to find positive and negative predictive values of ANA antibodies.For this purpose,twelve thousand nine hundred sixty-seven(12,967)patients were analyzed for ANA with four hundred sixty-eight(468)healthy samples tested as control and four thousand seven hundred three(4,703)patients tested for ds-DNA antibodies.Retrospective data of all samples tested by indirect immunofluorescence(IF)for ANA antibodies and dsDNA antibodies was collected.To address positive and negative predictive values another control group(autoimmunity not suspected)of serum samples was taken from the healthy population.For the first group,age,gender,ANA antibodies and ds-DNA antibodies results(both tests performed by IIF)data was collected from a computer record cell;for the second control group,ANA antibodies were performed by IIF.12,967 and 4,703 samples(Group 1)were tested for ANA antibodies and dsDNA antibodies,respectively,during this period.1,119(9%)and 99(2%)were found positive for ANA antibodies and ds DNA antibodies.Among these positive samples,850(76%)and 73(74%)were females respectively.Gender predisposition towards autoimmunity(ANA)was found significant with a P value of(P=0.001).Relation of age was also found significant with anti-ANA antibodies with a P value of(P=0.001).This study shows a negative correlation between age(P=0.025)and gender(P=0.001)with anti-dsDNA which is also significant.High prevalence was found below the mean age of 38 years(SD±16.635)for ANA antibodies and the mean age of 35 years(SD±15.066)for ds-DNA antibodies.The age of ANA antibodies and dsDNA antibodies positive patients ranged from 1 year old to 98 years old and 2 years old to 95 years old respectively.In the second(autoimmunity-free)control group,a total of 468 samples were tested for ANA antibodies and 9(2%)were found positive.Positive predictive value(PPV)was 8.6%and negative predictive value(NPV)was 98%.ANA is a sensitive test for autoimmunity and it is significantly related to female gender and increasing age.The low prevalence of ANA antibodies among clinically suspected cases suggests that rationalization of test prescriptions is needed.Anti-ds-DNA is also a sensitive test for diagnosis of SLE and it is significantly related to female gender and increasing age.

Targeting Cancers: Uncovering the Potential Roles of Potato Tissue Culture as Anti-Cancer Agents - A Secondary Publication

Plant tissue culture is a technique that enhances the quality and quantity of potatoes. Potatoes are a significant crop and are primarily used in the world. It is a staple food in many countries, where millions of tonnes are produced annually. It is an essential source of many nutrients, such as proteins, carbohydrates, vitamins, and beta-carotene. In addition, potatoes are being used as therapeutic agents against cancer and other human diseases as well. Potatoes are on the third list after wheat and rice. To overcome food shortages and malnutrition, there are two methods used for producing potatoes: the first is sexual, which is seed propagation, and the second is asexual, which is plant tissue culture propagation. Conventional potato breeding is a uniform method, but it is unsafe because there is a risk of pathogen attack. In a laboratory setting, the tissue culture of potatoes produced millions of plants with nutrient-rich medium under controlled environmental conditions that prevent pest attacks. Some environmental stresses, such as salinity and water scarcity, affect potato yield and production;however, applying nanoparticles like organic, inorganic, and silicon dioxide enhances potato quality and combats stress. Biotechnology has proven to be helpful in addressing all these issues. This review discusses the significance of potatoes, their production through the tissue culture technique, and the application of nanoparticles to improve the growth, and impact of potatoes on human health.

Advancing Cancer Stem Cell-Targeted Therapeutic Applications

Cancer stem cells(CSCs),first identified in blood cancers,are increasingly recognized as significant biomarkers and targets in tumor therapy due to their metastatic potential and role in cancer recurrence.Recent research has demonstrated the dedication of scientists in targeting CSCs to explore novel therapeutic strategies.Many types of cancer exhibit metastasis,heterogeneity,and resistance to treatment,all of which are influenced by CSCs.These cells utilize various transcription factors and signaling pathways to carry out these functions.By identifying and understanding these pathways,new therapeutic breakthroughs can be achieved.Thus,targeting cancer stem cells holds great potential and importance in cancer treatment.Moreover,CSCs offer promising avenues for treating otherwise incurable diseases.However,targeting CSCs presents challenges such as immunological rejection and disease recurrence.Advancing research into CSCs may reveal new insights in the fight against cancer and ultimately improve human health.This review explores the roles of CSCs in cancer development and treatment,aiming to uncover new therapeutic approaches.

Core-shell particles of C-doped CdS and graphene: A noble metal-free approach for efficient photocatalytic H_(2) generation

To achieve efficient photocatalytic H_(2) generation from water using earth-abundant and cost-effective materials,a simple synthesis method for carbon-doped CdS particles wrapped with graphene(C-doped CdS@G)is reported.The doping effect and the application of graphene as cocatalyst for CdS is studied for photocatalytic H_(2) generation.The most active sample consists of CdS and graphene(CdS-0.15G)exhibits promising photocatalytic activity,producing 3.12 mmol g^-(1) h^-(1) of H_(2) under simulated solar light which is^4.6 times superior than pure CdS nanoparticles giving an apparent quantum efficiency(AQY)of 11.7%.The enhanced photocatalytic activity for H_(2) generation is associated to the narrowing of the bandgap,enhanced light absorption,fast interfacial charge transfer,and higher carrier density(N_(D))in C-doped CdS@G samples.This is achieved by C doping in CdS nanoparticles and the formation of a graphene shell over the C-doped CdS nanoparticles.After stability test,the spent catalysts sample was also characterized to investigate the nanostructure.

Cluster-Based Group Mobility Support for Smart IoT

IPv6 over Low Power Wireless Personal Area Network(6LoWPAN)connects the highly constrained sensor nodes with the internet using the IPv6 protocol.6LoWPAN has improved the scalability of the Internet of Things(IoTs)infrastructure and allows mobile nodes to send packets over the IEEE 802.15.4 wireless network.Several mobility managements schemes have been suggested for handling the registration and handover procedures in 6LoWPAN.However,these schemes have performance constraints,such as increased transmission cost,signalling overhead,registration,and handover latency.To address these issues,we propose a novel cluster-based group mobility scheme(CGM6)for 6LoWPAN.To reduce the signalling cost in the CGM6 scheme,we propose to combine the functions of the Authentication,Authorization and Accounting(AAA)server and Local Mobility Anchor(LMA)in AMAG6(AAA+MAG for 6LoWPAN).AMAG6 acts as a cluster head and exchange its information directly with its neighbouring AMAG6 during the mobility.Furthermore,AMAG6 is responsible for the binding and authentication process.To reduce the transmission cost,we also propose enhancements in registration and Handover procedures.The performance of CGM6 is evaluated through extensive simulations.The simulation results show that CGM6 has reduced the handoff latency by 32%,registration delay by 11%and transmission cost by 37%compared to the state-of-the-art mobility management schemes.

Single and Mitochondrial Gene Inheritance Disorder Prediction Using Machine Learning

One of the most difficult jobs in the post-genomic age is identifying a genetic disease from a massive amount of genetic data.Furthermore,the complicated genetic disease has a very diverse genotype,making it challenging to find genetic markers.This is a challenging process since it must be completed effectively and efficiently.This research article focuses largely on which patients are more likely to have a genetic disorder based on numerous medical parameters.Using the patient’s medical history,we used a genetic disease prediction algorithm that predicts if the patient is likely to be diagnosed with a genetic disorder.To predict and categorize the patient with a genetic disease,we utilize several deep and machine learning techniques such as Artificial neural network(ANN),K-nearest neighbors(KNN),and Support vector machine(SVM).To enhance the accuracy of predicting the genetic disease in any patient,a highly efficient approach was utilized to control how the model can be used.To predict genetic disease,deep and machine learning approaches are performed.The most productive tool model provides more precise efficiency.The simulation results demonstrate that by using the proposed model with the ANN,we achieve the highest model performance of 85.7%,84.9%,84.3%accuracy of training,testing and validation respectively.This approach will undoubtedly transform genetic disorder prediction and give a real competitive strategy to save patients’lives.

Computationally predicted pathogenic USP9X mutation identified in infertile men does not affect spermatogenesis in mice

Infertility is a major health issue,affecting approximately 15%of couples of child-bearing age.Although nearly half of idiopathic infertility cases are assumed to have a genetic basis,the underlying causes remain largely unknown in most infertile men.

Data Fusion Architecture Empowered with Deep Learning for Breast Cancer Classification

Breast cancer(BC)is the most widespread tumor in females worldwide and is a severe public health issue.BC is the leading reason of death affecting females between the ages of 20 to 59 around the world.Early detection and therapy can help women receive effective treatment and,as a result,decrease the rate of breast cancer disease.The cancer tumor develops when cells grow improperly and attack the healthy tissue in the human body.Tumors are classified as benign or malignant,and the absence of cancer in the breast is considered normal.Deep learning,machine learning,and transfer learning models are applied to detect and identify cancerous tissue like BC.This research assists in the identification and classification of BC.We implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification(BCIC),which are machine learning-based models,by evaluating them in the form of comparative research.We used 3 datasets,A,B,and C.We fuzzed these datasets and got 2 datasets,A2C and B3C.Dataset A2C is the fusion of A,B,and C with 2 classes categorized as benign and malignant.Dataset B3C is the fusion of datasets A,B,and C with 3 classes classified as benign,malignant,and normal.We used customized AlexNet according to our datasets and BCIC in our proposed model.We achieved an accuracy of 86.5%on Dataset B3C and 76.8%on Dataset A2C by using AlexNet,and we achieved the optimum accuracy of 94.5%on Dataset B3C and 94.9%on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning rate.We proposed fuzzed dataset model using transfer learning.We fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique.

Ultra Fast Spray Cooling and Critical Droplet Daimeter Estimation from Cooling Rate

Spray cooling is an effective tool to dissipate high heat fluxes from hot surfaces. This paper thoroughly investigates the effects of spray parameters on the cooling time and cooling rate under varying inlet pressure using water as the coolant. Cylindrical samples of stainless steel with constant diameter, D = 25 mm, and thickness δ: 8.5 mm, 13 mm, 17.5 mm and 22 mm were investigated. Critical droplet diameter to achieve an ultrafast cooling rate of 300°C/s was estimated by using analytical model for samples of varying thickness. At an inlet pressure of 0.8 MPa, maximum cooling rates of 424.2°C/s, 502.81°C/s and 573.1°C/s were achieved for wall super heat ΔT = 600°C, 700°C and 800°C respectively.

Nuclear design of an integrated small modular reactor based on the APR-1400 for RO desalination purposes

The United Arab Emirates lacks conventional water resources and relies primarily on desalination plants powered by fossil fuels to produce fresh water.Nuclear desalination is a proven technology,cost-competitive,and sustainable option capable of integrating the existing largescale desalination plants to produce both freshwater and electricity.However,Small Modular Reactors(SMRs)are promising designs with advanced simplified configurations and inherent safety features.In this study,an Integrated Desalination SMR that produces thermal energy compatible with the capacity of a fossil fuel-powered desalination plant in the UAE was designed.First,the APR-1400 reactor core was used to investigate two 150 MWthconceptual SMR core designs,core A and core B,based on two-dimensional parameters,radius,and height.Then,the CASMO-4 lattice code was used to generate homogenized few-group constants for optimized fuel assembly loading patterns.Finally,to find the best core configuration,SIMULATE-3 was used to calculate the core key physics parameters such as power distribution,reactivity coefficients,and critical boron concentration.In addition,different reflector materials were investigated to compensate for the expected high leakage of the small-sized SMR cores.The pan shape core B model(142.6132 cm diameter,100 cm height,and radially reflected by Stainless Steel)was selected as the best core configuration based on its calculated physics parameters.Core B met the design and safety criteria and indicated low total neutron leakage of 11.60%and flat power distribution with 1.50 power peaking factor.Compared to core A,it has a more negative MTC value of-6.93 pcm/°F with lower CBC.In a 2-batch scheme,the fuel is discharged at 42.25 GWd/MTU burnup after a long cycle length of 1.58 years.The core B model offers the highest specific power of 36.56 kW/kgU while utilizing the smallest heavy metal mass compared with the SMART and NuScale models.

Genome-wide association analysis for stripe rust resistance in spring wheat(Triticum aestivum L.) germplasm

Stripe rust is a continuous threat to wheat crop all over the world.It causes considerable yield losses in wheat crop every year.Continuous deployment of adult plant resistance(APR)genes in newly developing wheat cultivars is the most judicious strategy to combat this disease.Herein,we dissected the genetics underpinning stripe rust resistance in Pakistani wheat germplasm.An association panel of 94 spring wheat genotypes was phenotyped for two years to score the infestation of stripe rust on each accession and was scanned with 203 polymorphic SSRs.Based on D’measure,linkage disequilibrium(LD)exhibited between loci distant up to 45 c M.Marker-trait associations(MTAs)were determined using mixed linear model(MLM).Total 31 quantitative trait loci(QTLs)were observed on all 21 wheat chromosomes.Twelve QTLs were newly discovered as well as 19 QTLs and 35 previously reported Yr genes were validated in Pakistani wheat germplasm.The major QTLs were QYr.uaf.2 AL and QYr.uaf.3 BS(PVE,11.9%).Dissection of genes from the newly observed QTLs can provide new APR genes to improve genetic resources for APR resistance in wheat crop.

Implications of twinning on the microstructure development,crystallographic texture and mechanical performance of Mg alloys-a critical review

Deformation twinning is profusely activated in the Mg alloys due to lower critical resolved shear stress(CRSS) compared to the non-basal slip systems(prismatic and pyramidal ) and plays a significant role in texture reorientation, grain refinement and enhancement of mechanical performance. Twinning is a sequential process comprising twin nucleation, twin propagation and twin growth, hence several intrinsic and extrinsic parameters that facilitate or suppress the process have been critically reviewed. The dependence of twinning on the grain size, deformation temperature, favorable grain orientation and shear strain have been thoroughly discussed in the context of published literature and an attempt has been made to provide a benchmark conclusive finding based on the majority of works. Furthermore, the subsequent effect of twinning on the mechanical performance of Mg alloys, including ductility, formability and tension-compression asymmetry has been discussed in detail. Lastly, the stability of twins, including stress and thermal stability, is summarized and critical issues related to pertinent bottlenecks have been addressed.

Solute drag-controlled grain growth in magnesium investigated by quasi in-situ orientation mapping and level-set simulations

Critical properties of metallic materials,such as the yield stress,corrosion resistance and ductility depend on the microstructure and its grain size and size distribution.Solute atoms that favorably segregate to grain boundaries produce a pinning atmosphere that exerts a drag pressure on the boundary motion,which strongly affects the grain growth behavior during annealing.In the current work,the characteristics of grain growth in an annealed Mg-1 wt.%Mn-1 wt.%Nd magnesium alloy were investigated by advanced experimental and modeling techniques.Systematic quasi in-situ orientation mappings with a scanning electron microscope were performed to track the evolution of local and global microstructural characteristics as a function of annealing time.Solute segregation at targeted grain boundaries was measured using three-dimensional atom probe tomography.Level-set computer simulations were carried with different setups of driving forces to explore their contribution to the microstructure development with and without solute drag.The results showed that the favorable growth advantage for some grains leading to a transient stage of abnormal grain growth is controlled by several drivers with varying importance at different stages of annealing.For longer annealing times,residual dislocation density gradients between large and smaller grains are no longer important,which leads to microstructure stability due to predominant solute drag.Local fluctuations in residual dislocation energy and solute concentration near grain boundaries cause different boundary segments to migrate at different rates,which affects the average growth rate of large grains and their evolved shape.

Classification of Diabetic Macular Edema and Its Stages Using Color Fundus Image

Diabetic macular edema （DME） is a retinal thickening involving the center of the macula. It is one of the serious eye diseases which affects the central vision and can lead to partial or even complete visual loss. The only cure is timely diagnosis, prevention, and treatment of the disease. This paper presents an automated system for the diagnosis and classification of DME using color fundus image. In the proposed technique, first the optic disc is removed by applying some preprocessing steps. The preprocessed image is then passed through a classifier for segmentation of the image to detect exudates. The classifier uses dynamic thresholding technique by using some input parameters of the image. The stage classification is done on the basis of anearly treatment diabetic retinopathy study （ETDRS） given criteria to assess the severity of disease. The proposed technique gives a sensitivity, specificity, and accuracy of 98.27%, 96.58%, and 96.54%, respectively on publically available database.

Studies on Plant Population and Stand Establishment Techniques for Increasing Productivity of Rice in Dera Ismail Khan, Pakistan

Rice production in Pakistan is constraint by many factors pertaining to prevalent planting techniques. A research on the feasibility of new planting techniques （direct seeding on flat, transplanting on flat, direct seeding on ridges, transplanting on ridges and parachute planting） in transplanted and direct wet-seeded rice was undertaken at Dera Ismail Khan region of Pakistan＇s North West Frontier Province during 2002 and 2003. Among the planting techniques, the best performance for the yield formation and economic evaluation was noted for transplanting on flat during both years. Chinese parachute planting technology also showed very promising results in most of the parameters. Direct seeding on ridges could not excel transplanting on flat and parachute planting during both cropping seasons. The findings concluded the feasibility of parachute planting technology along with traditional rice transplanting on flat over all other planting techniques being practiced in the area.

A Deep Learning Approach for Prediction of Protein Secondary Structure

The secondary structure of a protein is critical for establishing a link between the protein primary and tertiary structures.For this reason,it is important to design methods for accurate protein secondary structure prediction.Most of the existing computational techniques for protein structural and functional prediction are based onmachine learning with shallowframeworks.Different deep learning architectures have already been applied to tackle protein secondary structure prediction problem.In this study,deep learning based models,i.e.,convolutional neural network and long short-term memory for protein secondary structure prediction were proposed.The input to proposed models is amino acid sequences which were derived from CulledPDB dataset.Hyperparameter tuning with cross validation was employed to attain best parameters for the proposed models.The proposed models enables effective processing of amino acids and attain approximately 87.05%and 87.47%Q3 accuracy of protein secondary structure prediction for convolutional neural network and long short-term memory models,respectively.

Effects of dietary vitamin E on male reproductive system

Vitamin E is known as important antioxidant to protect the reproductive system. The free radicals are continuously produced in last few years due to metabolic and nutritional deficiencies. These free radicals are responsible for the production of oxidative stress in animal bodies. This production of extensive amount of oxidative stress caused the detrimental effects on the sperm and various other male parameters. This imbalance between the antioxidants and oxidative stress, leads to the condition of infertility in male. Antioxidants play an important role for eliminating of these free radicals. Vitamin E is one of the best antioxidants for the removal of oxidative stress in male reproductive system. Its use increases the reproductive functions and efficiency of male reproductive system. The deficiency of this vitamin leads to degeneration of germinal epithelium and Leydig cells in seminiferous tubules. The use of selenium and vitamin E has the synergistic effects on the male reproductive system. The objective of this review was to collect the beneficial roles of this vitamin along selenium on reproductive system of birds and different animals. This review will also collect the different doses along the beneficial roles on different parameters of male reproductive system.