The agricultural sector is notably affected by climate change,especially soybeans,which may face diminished yields because of severe water shortages.The evaluation of germplasm at morphological and molecular levels is...The agricultural sector is notably affected by climate change,especially soybeans,which may face diminished yields because of severe water shortages.The evaluation of germplasm at morphological and molecular levels is an important pre-breeding step for crop improvement.This study employed 10 simple sequence repeat(SSR)markers to examine 60 soybean genotypes in the quest for drought-resistant lines during 2022–23.The results of the screening experiment(PEG-6000)revealed that the soybean genotypes SPS13,SPS195,PGRB83,and 39982 exhibited significant correlations in growth parameters.The results of molecular characterization indicated that five out of ten molecular markers,specifically Satt373,Satt454,Satt471,Satt478,and Satt581,exhibited distinct banding patterns along with elevated levels of genetic diversity and heterozygosity.The phylogenetic analysis findings indicated that soybean genotypes were categorized into many clusters,with at least six genotypes located in cluster 5 and the most seventeen genotypes in cluster 7.The results obtained from principal component analysis indicated that PC1 explained up to 44.7%of the variance,while PC2 accounted for 17.3%.The results of the heatmap indicated that PGBR83 exhibited the highest expression in plant height,GP39982 and SPS109 in chlorophyll content,GP39982 in proline accumulation,and SPS2,GP40025,SPS69,and GP40174 in protein content,number of pods per plant,and yield per plant,whereas GP40116 and PGRA83 demonstrated consistently low expression.The results of biochemical analysis indicated that the soybean genotypes SPS13,PGRA83,SPS176,40158,SPS162,SPS195,SPS175,SPS109,and SPS80 were identified as superior sources of protein and oil content,along with genotypes such as PGRB55,SPS177,40116,and 40111,which exhibited a significant increase under drought stress conditions.The findings of this research provide complete information derived from molecular approaches on soybean genotypes,which might assist breeders in selecting parental lines to generate drought-tolerant soybean cultivars in the future.展开更多
Soil metal pollution is a global issue due to its toxic nature affecting ecosystems and human health. This has become a concern since metals are non-biodegradable and toxic. Most of the reclamation methods currently u...Soil metal pollution is a global issue due to its toxic nature affecting ecosystems and human health. This has become a concern since metals are non-biodegradable and toxic. Most of the reclamation methods currently used for soils rely on the use of physical and chemical means, which tend to be very expensive and result in secondary environmental damage. However, microbe-aided phytoremediation is gaining attention as it is an eco-friendly, affordable, and technically advanced method to restore the ecosystem. It is essential to understand the complex interaction between plants and microbes. The primary function of plant growth-promoting bacteria (PGPB) is to stimulate plant development, aid in metal elimination, and reduce their bioavailability in the soil. These microbes regulate phytohormones, stimulate processes such as phytoextraction and phyto-stabilization, and improve the uptake of essential nutrients, such as nitrogen and phosphorus. PGPBs secrete a range of enzymes and chemicals, fix nitrogen, solubilize minerals, increase the bioavailability of nutrients under diverse biological environments with high salinities, excessive metal-contaminated soil, and organic pollutants, increase the soil fertility and help in the reclamation of agriculture and regenerate the native flora. The integration of CRISPR-Cas9 gene-editing technology with microbial-aided phytoremediation and the use of genetically modified microbes with nanomaterials further enhance the efficacy of the approaches in polluted environments for sustainable restoration of the soil.展开更多
Crime hotspot detection is essential for law enforcement agencies to allocate resources effectively,predict potential criminal activities,and ensure public safety.Traditional methods of crime analysis often rely on ma...Crime hotspot detection is essential for law enforcement agencies to allocate resources effectively,predict potential criminal activities,and ensure public safety.Traditional methods of crime analysis often rely on manual,time-consuming processes that may overlook intricate patterns and correlations within the data.While some existing machine learning models have improved the efficiency and accuracy of crime prediction,they often face limitations such as overfitting,imbalanced datasets,and inadequate handling of spatiotemporal dynamics.This research proposes an advanced machine learning framework,CHART(Crime Hotspot Analysis and Real-time Tracking),designed to overcome these challenges.The proposed methodology begins with comprehensive data collection from the police database.The dataset includes detailed attributes such as crime type,location,time and demographic information.The key steps in the proposed framework include:Data Preprocessing,Feature Engineering that leveraging domain-specific knowledge to extract and transform relevant features.Heat Map Generation that employs Kernel Density Estimation(KDE)to create visual representations of crime density,highlighting hotspots through smooth data point distributions and Hotspot Detection based on Random Forest-based to predict crime likelihood in various areas.The Experimental evaluation demonstrated that CHART shows superior performance over benchmark methods,significantly improving crime detection accuracy by getting 95.24%for crime detection-I(CD-I),96.12%for crime detection-II(CD-II)and 94.68%for crime detection-III(CD-III),respectively.By designing the application with integrating sophisticated preprocessing techniques,balanced data representation,and advanced feature engineering,the proposed model provides a reliable and practical tool for real-world crime analysis.Visualization of crime hotspots enables law enforcement agencies to strategize effectively,focusing resources on high-risk areas and thereby enhancing overall crime prevention and response efforts.展开更多
Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic methods.Traditional diagnostics face limitations such as reliance on single-modality data and vu...Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic methods.Traditional diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality images.Additionally,these methods often require significant time and expertise,making them less accessible in resource-limited settings.Emerging technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare costs.This study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant features.Classification is performed using Optimized TCGN.This approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health monitoring.The proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality issues.These findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.展开更多
Reduced early crop growth and limited branching are amongst yield limiting factors of linola. Field response of seed priming treatments viz. 50 mmol L^-1 salicylic acid （SA）, 2.2% CaCl2 and 3.3% moringa leaf extract...Reduced early crop growth and limited branching are amongst yield limiting factors of linola. Field response of seed priming treatments viz. 50 mmol L^-1 salicylic acid （SA）, 2.2% CaCl2 and 3.3% moringa leaf extract （MLE） including untreated dry and hydropriming controls was evaluated on early crop growth and yield performance of linola. Osmopriming with CaCl2 reduced emergence time and produced the highest seedling fresh and dry weights including Chl. a contents. Osmopriming with CaCl2 reduced crop branching and flowering and maturity times and had the maximum plant height, number of branches, tillers, pods and seeds per pod followed by MLE. Increase in seed weight, biological and seed yields was 9.30, 34.16 and 39.49%, harvest index （4.12%） and oil contents （13.39%） for CaCl2 osmopriming. Positive relationship between emergence and seedling vigor traits, 100-seed weight, seed yield with maturity time, 100-seed weight and seed yield were found. The study concludes that seed osmopriming with CaC12 or MLE can play significant role to improve early crop growth and seed yields of linola.展开更多
The current study aims to investigate the population variation and food habits of ranid frogs in the rice-based cropping system in District Gujranwala,Pakistan.The population in the study area was estimated using capt...The current study aims to investigate the population variation and food habits of ranid frogs in the rice-based cropping system in District Gujranwala,Pakistan.The population in the study area was estimated using capture,mark and release method whereas food habits of the species were studied by analysis of stomach contents.The results showed the highest average population was found during August 2009(93.10±18.64/ha) while the lowest from December 2008 to February 2009.Maximum seasonal populations existed in summer 2009,whereas winter 2008 sizes were at a minimum.Stomach content analysis of the species revealed percent frequency(% F) of occurrence of insects(80.3),earthworms(28.5),whole frogs(15.8),bone pieces(22.5),rodents(1.66),vegetation(5.0),soil particles(13.3) and some unidentified material(7.5) in all the stomach samples.Most frequently consumed prey items were insects(30% by volume),although frogs also preyed upon conspecifics and rodents.Insects recovered from the stomach contents were identified as belonging to Orthoptera,Lepidoptera,Coleoptera,Diptera,Odonata and Homoptera as well as the class Archnida.Insects recovered from the stomach contents were compared to those captured from the study area.展开更多
The Sentinel-2 A satellite having embedded advantage of red edge spectral bands offers multispectral imageries with improved spatial,spectral and temporal resolutions as compared to the other contemporary satellites p...The Sentinel-2 A satellite having embedded advantage of red edge spectral bands offers multispectral imageries with improved spatial,spectral and temporal resolutions as compared to the other contemporary satellites providing medium resolution data.Our study was aimed at exploring the potential of Sentinel-2 A imagery to estimate Above Ground Biomass(AGB) of Subtropical Pine Forest in Pakistan administered Kashmir.We developed an AGB predictive model using field inventory and Sentinel 2 A based spectral and textural parameters along with topographic features derived from ALOS Digital Elevation Model(DEM).Field inventory data was collected from 108 randomly distributed plots(0.1 ha each) across the study area.The stepwise linear regression method was employed to investigate the potential relationship between field data and corresponding satellite data.Biomass and carbon mapping of the study area was carried out through established AGB estimation model with R(o.86),R2(0.74),adjusted R2(0.72) and RMSE value of 33 t/ha.Our results showed that first order textures(mean,standard deviation and variance) significantly contributed in AGB predictive modeling while only one spectral band ratio made contribution from spectral domain.Our study leads to the conclusion that Sentinel-2 A optical data is a potential source for AGB estimation in subtropical pine forest of the area of interest with added benefit of its free of cost availability,higher quality data and long-term continuity that can be utilized for biomass carbon distribution mapping in the resource constraint study area for sustainable forest management.展开更多
Forest soils have high carbon densities compared to other land-uses.Soil carbon sequestration is important to reduce CO 2 concentrations in the atmosphere.An eff ective climate change mitigation strategy involves limi...Forest soils have high carbon densities compared to other land-uses.Soil carbon sequestration is important to reduce CO 2 concentrations in the atmosphere.An eff ective climate change mitigation strategy involves limiting the emissions of greenhouse gases from soils.Khyber Pakhtunkhwa is the most forested province of Pakistan,hosting about one-third of the country’s 4.5×106 ha forest area.Soil organic carbon in the province’s forests was estimated through a fi eld-based study carried out during 2014–17 covering the whole province.Data was collected from 373 sample plots laid out in diff erent forest types using a stratifi ed cluster sampling technique.The total quantity of soil organic carbon was estimated at 59.4×106 t with an average of 52.4±5.3 t/ha.About 69%of the total soil carbon is present in temperate forests.Subtropical broad-leaved and subtropical pine forests constitute 11.4%and 8.8%of the soil carbon stock respectively.Similarly,subalpine and oak forests have respective shares of 5.1%and 5.7%in the soil carbon pool.The lowest carbon stock(0.1%)was found in dry-tropical thorn forests.The highest soil carbon density was found in subalpine forests(69.5±7.2 t/ha)followed by moist temperate forests(68.5±6.7 t/ha)and dry temperate forests(60.7±6.5 t/ha).Oak forests have carbon density of 43.4±7.1 t/ha.Subtropical pine,subtropical broad-leaved and dry tropical thorn forests have soil carbon densities of 36.3±3.7,32.8±6.2 and 31.5±3.5 t/ha,respectively.The forests of the Khyber Pakhtunkhwa province have substantial amounts of soil carbon which must be conserved for climate change mitigation and maintenance of sound forest health.展开更多
Objective To evaluate the ovarian response to the gonadotrophin （Gn） in the COH and observe the outcome of lVF for the patients with endometriomas. Methods A retrospective analysis of 32 patients with endometrioma u...Objective To evaluate the ovarian response to the gonadotrophin （Gn） in the COH and observe the outcome of lVF for the patients with endometriomas. Methods A retrospective analysis of 32 patients with endometrioma undergoing IVFET. It included 71 cycles, and 59 cycles in 32 patients with tubal factor associated infertility were as the control. Results There were statistically significant differences between the two groups in the cancelling rate （P〈0.01）, the E： concentration in the day of hCG injection （P〈0.05）, retrieval eggs（P〈0.001）, rate of fertilization （P〈0.05）, rate of cleavage （P〈0.05）, obtained embryos （P〈0. 001）. There were no statistically significant differences in the clinical pregnancy rate, implantation rate and delivery rate, P all〉0.05. Conclusion The patients with endometriomas had a poor response to the Gn in the COH. The endometrial accessibility in patients with endometriomas seemed not to be affected by the presence of endometriomas. But considering the higher cancelling rate, the prognostic for the patients with endometriomas was worth than the patients with tubal factor associated infertility.展开更多
In energy deficient world, cellulases play a major role for the production of alternative energy resources utilizing lignocellulosic waste materials for bioethanol and biogas production. This study highlights fungal a...In energy deficient world, cellulases play a major role for the production of alternative energy resources utilizing lignocellulosic waste materials for bioethanol and biogas production. This study highlights fungal and bacterial strains for the production of cellulases and its industrial applications. Solid State Fermentation (SSF) is more suitable process for cellulase production as compared to submerge fermentation techniques. Fungal cellulosomes system for the production of cellulases is more desirable and resistant to harsh environmental conditions. Trichoderma species are considered as most suitable candidate for cellulase production and utilization in industry as compared to Aspergillus and Humicola species. However, genetically modified strains of Aspergillus have capability to produce cellulase in relatively higher amount. Bacterial cellulase are more resistant to alkaline and thermophile conditions and good candidate in laundries. Cellulases are used in variety of industries such as textile, detergents and laundries, food industry, paper and pulp industry and biofuel production. Thermally stable modified strains of fungi and bacteria are good future prospect for cellulase production.展开更多
In the present study, histopathology of three varieties of sesame TS 3, TS 5 and SG 27 infected with Alternaria alternata was carried out to understand the mechanism of fungal infection and penetration in sesame plant...In the present study, histopathology of three varieties of sesame TS 3, TS 5 and SG 27 infected with Alternaria alternata was carried out to understand the mechanism of fungal infection and penetration in sesame plant as well as to determine the histological manifestation in sesame cells by light microscopy. Fungus was identified in infected tissues as a dark bluish black with toluidine blue O staining. Light microscopic examination of sesame stem showed that the fungus was present in epidermis, hypodermis and cortical parenchyma tissue as the symptoms became visible by naked eye ten days after inoculation (DAI). As the disease progress, the fungus moved from cortical parenchyma to vascular bundle, xylem and phloem. Later on, it completely overlapped the vascular bundle and entered in pith. When necrotic lesion appeared, fungus was present abundantly in epidermis, hypodermis, cortical parenchyma, vascular bundles and in pith. Due to its excessive growth and complete overlapping of cells, disorganization or destruction of cells of sesame took place. It was concluded that the Alternaria alternata was not a tissue limited pathogen instead of this it spread in to all tissues of stem from epidermis to pith.展开更多
The severity of traffic accidents is a serious global concern,particularly in developing nations.Knowing the main causes and contributing circumstances may reduce the severity of traffic accidents.There exist many mac...The severity of traffic accidents is a serious global concern,particularly in developing nations.Knowing the main causes and contributing circumstances may reduce the severity of traffic accidents.There exist many machine learning models and decision support systems to predict road accidents by using datasets from different social media forums such as Twitter,blogs and Facebook.Although such approaches are popular,there exists an issue of data management and low prediction accuracy.This article presented a deep learning-based sentiment analytic model known as Extra-large Network Bi-directional long short term memory(XLNet-Bi-LSTM)to predict traffic collisions based on data collected from social media.Initially,a Tweet dataset has been formed by using an exhaustive keyword-based searching strategy.In the next phase,two different types of features named as individual tokens and pair tokens have been obtained by using POS tagging and association rule mining.The output of this phase has been forwarded to a three-layer deep learning model for final prediction.Numerous experiment has been performed to test the efficiency of the proposed XLNet-Bi-LSTM model.It has been shown that the proposed model achieved 94.2%prediction accuracy.展开更多
People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest affairs.People share their thoughts and feelings about various ...People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest affairs.People share their thoughts and feelings about various topics,including products,news,blogs,etc.In user reviews and tweets,sentiment analysis is used to discover opinions and feelings.Sentiment polarity is a term used to describe how sentiment is represented.Positive,neutral and negative are all examples of it.This area is still in its infancy and needs several critical upgrades.Slang and hidden emotions can detract from the accuracy of traditional techniques.Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories.Some existing strategies are domain-specific.The proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion words.Later,classification was performed using BER.The proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and Twitter.The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.展开更多
基金funding with Ref.No.PMAS-AAUR/ORIC/2018 dated 08-03-2021 from Office of Research,Innovation and Commercilization,Pir Mahr Ali Shah Air Agriculture University,Rawalpindi,Pakistan for this study.
文摘The agricultural sector is notably affected by climate change,especially soybeans,which may face diminished yields because of severe water shortages.The evaluation of germplasm at morphological and molecular levels is an important pre-breeding step for crop improvement.This study employed 10 simple sequence repeat(SSR)markers to examine 60 soybean genotypes in the quest for drought-resistant lines during 2022–23.The results of the screening experiment(PEG-6000)revealed that the soybean genotypes SPS13,SPS195,PGRB83,and 39982 exhibited significant correlations in growth parameters.The results of molecular characterization indicated that five out of ten molecular markers,specifically Satt373,Satt454,Satt471,Satt478,and Satt581,exhibited distinct banding patterns along with elevated levels of genetic diversity and heterozygosity.The phylogenetic analysis findings indicated that soybean genotypes were categorized into many clusters,with at least six genotypes located in cluster 5 and the most seventeen genotypes in cluster 7.The results obtained from principal component analysis indicated that PC1 explained up to 44.7%of the variance,while PC2 accounted for 17.3%.The results of the heatmap indicated that PGBR83 exhibited the highest expression in plant height,GP39982 and SPS109 in chlorophyll content,GP39982 in proline accumulation,and SPS2,GP40025,SPS69,and GP40174 in protein content,number of pods per plant,and yield per plant,whereas GP40116 and PGRA83 demonstrated consistently low expression.The results of biochemical analysis indicated that the soybean genotypes SPS13,PGRA83,SPS176,40158,SPS162,SPS195,SPS175,SPS109,and SPS80 were identified as superior sources of protein and oil content,along with genotypes such as PGRB55,SPS177,40116,and 40111,which exhibited a significant increase under drought stress conditions.The findings of this research provide complete information derived from molecular approaches on soybean genotypes,which might assist breeders in selecting parental lines to generate drought-tolerant soybean cultivars in the future.
基金supported by the Yibin Science and Technology Plan(2022NY011).
文摘Soil metal pollution is a global issue due to its toxic nature affecting ecosystems and human health. This has become a concern since metals are non-biodegradable and toxic. Most of the reclamation methods currently used for soils rely on the use of physical and chemical means, which tend to be very expensive and result in secondary environmental damage. However, microbe-aided phytoremediation is gaining attention as it is an eco-friendly, affordable, and technically advanced method to restore the ecosystem. It is essential to understand the complex interaction between plants and microbes. The primary function of plant growth-promoting bacteria (PGPB) is to stimulate plant development, aid in metal elimination, and reduce their bioavailability in the soil. These microbes regulate phytohormones, stimulate processes such as phytoextraction and phyto-stabilization, and improve the uptake of essential nutrients, such as nitrogen and phosphorus. PGPBs secrete a range of enzymes and chemicals, fix nitrogen, solubilize minerals, increase the bioavailability of nutrients under diverse biological environments with high salinities, excessive metal-contaminated soil, and organic pollutants, increase the soil fertility and help in the reclamation of agriculture and regenerate the native flora. The integration of CRISPR-Cas9 gene-editing technology with microbial-aided phytoremediation and the use of genetically modified microbes with nanomaterials further enhance the efficacy of the approaches in polluted environments for sustainable restoration of the soil.
基金appreciation to King Saud University for funding this work through Researchers Supporting Project number(RSPD2025R685),King Saud University,Riyadh,Saudi Arabia.
文摘Crime hotspot detection is essential for law enforcement agencies to allocate resources effectively,predict potential criminal activities,and ensure public safety.Traditional methods of crime analysis often rely on manual,time-consuming processes that may overlook intricate patterns and correlations within the data.While some existing machine learning models have improved the efficiency and accuracy of crime prediction,they often face limitations such as overfitting,imbalanced datasets,and inadequate handling of spatiotemporal dynamics.This research proposes an advanced machine learning framework,CHART(Crime Hotspot Analysis and Real-time Tracking),designed to overcome these challenges.The proposed methodology begins with comprehensive data collection from the police database.The dataset includes detailed attributes such as crime type,location,time and demographic information.The key steps in the proposed framework include:Data Preprocessing,Feature Engineering that leveraging domain-specific knowledge to extract and transform relevant features.Heat Map Generation that employs Kernel Density Estimation(KDE)to create visual representations of crime density,highlighting hotspots through smooth data point distributions and Hotspot Detection based on Random Forest-based to predict crime likelihood in various areas.The Experimental evaluation demonstrated that CHART shows superior performance over benchmark methods,significantly improving crime detection accuracy by getting 95.24%for crime detection-I(CD-I),96.12%for crime detection-II(CD-II)and 94.68%for crime detection-III(CD-III),respectively.By designing the application with integrating sophisticated preprocessing techniques,balanced data representation,and advanced feature engineering,the proposed model provides a reliable and practical tool for real-world crime analysis.Visualization of crime hotspots enables law enforcement agencies to strategize effectively,focusing resources on high-risk areas and thereby enhancing overall crime prevention and response efforts.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R435),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic methods.Traditional diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality images.Additionally,these methods often require significant time and expertise,making them less accessible in resource-limited settings.Emerging technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare costs.This study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant features.Classification is performed using Optimized TCGN.This approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health monitoring.The proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality issues.These findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.
文摘Reduced early crop growth and limited branching are amongst yield limiting factors of linola. Field response of seed priming treatments viz. 50 mmol L^-1 salicylic acid （SA）, 2.2% CaCl2 and 3.3% moringa leaf extract （MLE） including untreated dry and hydropriming controls was evaluated on early crop growth and yield performance of linola. Osmopriming with CaCl2 reduced emergence time and produced the highest seedling fresh and dry weights including Chl. a contents. Osmopriming with CaCl2 reduced crop branching and flowering and maturity times and had the maximum plant height, number of branches, tillers, pods and seeds per pod followed by MLE. Increase in seed weight, biological and seed yields was 9.30, 34.16 and 39.49%, harvest index （4.12%） and oil contents （13.39%） for CaCl2 osmopriming. Positive relationship between emergence and seedling vigor traits, 100-seed weight, seed yield with maturity time, 100-seed weight and seed yield were found. The study concludes that seed osmopriming with CaC12 or MLE can play significant role to improve early crop growth and seed yields of linola.
文摘The current study aims to investigate the population variation and food habits of ranid frogs in the rice-based cropping system in District Gujranwala,Pakistan.The population in the study area was estimated using capture,mark and release method whereas food habits of the species were studied by analysis of stomach contents.The results showed the highest average population was found during August 2009(93.10±18.64/ha) while the lowest from December 2008 to February 2009.Maximum seasonal populations existed in summer 2009,whereas winter 2008 sizes were at a minimum.Stomach content analysis of the species revealed percent frequency(% F) of occurrence of insects(80.3),earthworms(28.5),whole frogs(15.8),bone pieces(22.5),rodents(1.66),vegetation(5.0),soil particles(13.3) and some unidentified material(7.5) in all the stomach samples.Most frequently consumed prey items were insects(30% by volume),although frogs also preyed upon conspecifics and rodents.Insects recovered from the stomach contents were identified as belonging to Orthoptera,Lepidoptera,Coleoptera,Diptera,Odonata and Homoptera as well as the class Archnida.Insects recovered from the stomach contents were compared to those captured from the study area.
文摘The Sentinel-2 A satellite having embedded advantage of red edge spectral bands offers multispectral imageries with improved spatial,spectral and temporal resolutions as compared to the other contemporary satellites providing medium resolution data.Our study was aimed at exploring the potential of Sentinel-2 A imagery to estimate Above Ground Biomass(AGB) of Subtropical Pine Forest in Pakistan administered Kashmir.We developed an AGB predictive model using field inventory and Sentinel 2 A based spectral and textural parameters along with topographic features derived from ALOS Digital Elevation Model(DEM).Field inventory data was collected from 108 randomly distributed plots(0.1 ha each) across the study area.The stepwise linear regression method was employed to investigate the potential relationship between field data and corresponding satellite data.Biomass and carbon mapping of the study area was carried out through established AGB estimation model with R(o.86),R2(0.74),adjusted R2(0.72) and RMSE value of 33 t/ha.Our results showed that first order textures(mean,standard deviation and variance) significantly contributed in AGB predictive modeling while only one spectral band ratio made contribution from spectral domain.Our study leads to the conclusion that Sentinel-2 A optical data is a potential source for AGB estimation in subtropical pine forest of the area of interest with added benefit of its free of cost availability,higher quality data and long-term continuity that can be utilized for biomass carbon distribution mapping in the resource constraint study area for sustainable forest management.
基金This research is part of the doctoral dissertation of the fi rst author at PMAS Arid Agriculture University,Rawalpindi,Pakistan(AAUR).The authors are extremely grateful to Prof.Dr.Sarwat N.Mirza,former Vice-Chancellor of PMAS Arid Agriculture University,Rawalpindi,for his valuable inputs and support during the study period.Thanks are also extended to the staff of Forest Mensuration Branch,Pakistan Forest Institute,Peshawar for their help in data collection in the fi eld.
文摘Forest soils have high carbon densities compared to other land-uses.Soil carbon sequestration is important to reduce CO 2 concentrations in the atmosphere.An eff ective climate change mitigation strategy involves limiting the emissions of greenhouse gases from soils.Khyber Pakhtunkhwa is the most forested province of Pakistan,hosting about one-third of the country’s 4.5×106 ha forest area.Soil organic carbon in the province’s forests was estimated through a fi eld-based study carried out during 2014–17 covering the whole province.Data was collected from 373 sample plots laid out in diff erent forest types using a stratifi ed cluster sampling technique.The total quantity of soil organic carbon was estimated at 59.4×106 t with an average of 52.4±5.3 t/ha.About 69%of the total soil carbon is present in temperate forests.Subtropical broad-leaved and subtropical pine forests constitute 11.4%and 8.8%of the soil carbon stock respectively.Similarly,subalpine and oak forests have respective shares of 5.1%and 5.7%in the soil carbon pool.The lowest carbon stock(0.1%)was found in dry-tropical thorn forests.The highest soil carbon density was found in subalpine forests(69.5±7.2 t/ha)followed by moist temperate forests(68.5±6.7 t/ha)and dry temperate forests(60.7±6.5 t/ha).Oak forests have carbon density of 43.4±7.1 t/ha.Subtropical pine,subtropical broad-leaved and dry tropical thorn forests have soil carbon densities of 36.3±3.7,32.8±6.2 and 31.5±3.5 t/ha,respectively.The forests of the Khyber Pakhtunkhwa province have substantial amounts of soil carbon which must be conserved for climate change mitigation and maintenance of sound forest health.
文摘Objective To evaluate the ovarian response to the gonadotrophin （Gn） in the COH and observe the outcome of lVF for the patients with endometriomas. Methods A retrospective analysis of 32 patients with endometrioma undergoing IVFET. It included 71 cycles, and 59 cycles in 32 patients with tubal factor associated infertility were as the control. Results There were statistically significant differences between the two groups in the cancelling rate （P〈0.01）, the E： concentration in the day of hCG injection （P〈0.05）, retrieval eggs（P〈0.001）, rate of fertilization （P〈0.05）, rate of cleavage （P〈0.05）, obtained embryos （P〈0. 001）. There were no statistically significant differences in the clinical pregnancy rate, implantation rate and delivery rate, P all〉0.05. Conclusion The patients with endometriomas had a poor response to the Gn in the COH. The endometrial accessibility in patients with endometriomas seemed not to be affected by the presence of endometriomas. But considering the higher cancelling rate, the prognostic for the patients with endometriomas was worth than the patients with tubal factor associated infertility.
文摘In energy deficient world, cellulases play a major role for the production of alternative energy resources utilizing lignocellulosic waste materials for bioethanol and biogas production. This study highlights fungal and bacterial strains for the production of cellulases and its industrial applications. Solid State Fermentation (SSF) is more suitable process for cellulase production as compared to submerge fermentation techniques. Fungal cellulosomes system for the production of cellulases is more desirable and resistant to harsh environmental conditions. Trichoderma species are considered as most suitable candidate for cellulase production and utilization in industry as compared to Aspergillus and Humicola species. However, genetically modified strains of Aspergillus have capability to produce cellulase in relatively higher amount. Bacterial cellulase are more resistant to alkaline and thermophile conditions and good candidate in laundries. Cellulases are used in variety of industries such as textile, detergents and laundries, food industry, paper and pulp industry and biofuel production. Thermally stable modified strains of fungi and bacteria are good future prospect for cellulase production.
文摘In the present study, histopathology of three varieties of sesame TS 3, TS 5 and SG 27 infected with Alternaria alternata was carried out to understand the mechanism of fungal infection and penetration in sesame plant as well as to determine the histological manifestation in sesame cells by light microscopy. Fungus was identified in infected tissues as a dark bluish black with toluidine blue O staining. Light microscopic examination of sesame stem showed that the fungus was present in epidermis, hypodermis and cortical parenchyma tissue as the symptoms became visible by naked eye ten days after inoculation (DAI). As the disease progress, the fungus moved from cortical parenchyma to vascular bundle, xylem and phloem. Later on, it completely overlapped the vascular bundle and entered in pith. When necrotic lesion appeared, fungus was present abundantly in epidermis, hypodermis, cortical parenchyma, vascular bundles and in pith. Due to its excessive growth and complete overlapping of cells, disorganization or destruction of cells of sesame took place. It was concluded that the Alternaria alternata was not a tissue limited pathogen instead of this it spread in to all tissues of stem from epidermis to pith.
文摘The severity of traffic accidents is a serious global concern,particularly in developing nations.Knowing the main causes and contributing circumstances may reduce the severity of traffic accidents.There exist many machine learning models and decision support systems to predict road accidents by using datasets from different social media forums such as Twitter,blogs and Facebook.Although such approaches are popular,there exists an issue of data management and low prediction accuracy.This article presented a deep learning-based sentiment analytic model known as Extra-large Network Bi-directional long short term memory(XLNet-Bi-LSTM)to predict traffic collisions based on data collected from social media.Initially,a Tweet dataset has been formed by using an exhaustive keyword-based searching strategy.In the next phase,two different types of features named as individual tokens and pair tokens have been obtained by using POS tagging and association rule mining.The output of this phase has been forwarded to a three-layer deep learning model for final prediction.Numerous experiment has been performed to test the efficiency of the proposed XLNet-Bi-LSTM model.It has been shown that the proposed model achieved 94.2%prediction accuracy.
文摘People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest affairs.People share their thoughts and feelings about various topics,including products,news,blogs,etc.In user reviews and tweets,sentiment analysis is used to discover opinions and feelings.Sentiment polarity is a term used to describe how sentiment is represented.Positive,neutral and negative are all examples of it.This area is still in its infancy and needs several critical upgrades.Slang and hidden emotions can detract from the accuracy of traditional techniques.Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories.Some existing strategies are domain-specific.The proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion words.Later,classification was performed using BER.The proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and Twitter.The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.
