Kernel Set Research Articles

ZmSPL10, ZmSPL14 and ZmSPL26 act together to promote stigmatic papilla formation in maize through regulating auxin signaling and ZmWOX3A expression.

Maize silk is a specialized type of stigma, covered with numerous papillae for pollen grain capture. However, the developmental process of stigmatic papillae and the underlying regulatory mechanisms have remained largely unknown. Here, we combined the cytological, genetic and molecular studies to demonstrate that three homologous genes ZmSPL10, ZmSPL14 and ZmSPL26 play a central role in promoting stigmatic papilla formation in maize. We show that their triple knockout mutants are nearly complete lack of stigmatic papilla, resulting in a severe reduction in kernel setting. Cellular examination reveals that stigmatic papilla is developed from a precursor cell, which is the smaller daughter cell resulting from asymmetric cell division of a silk epidermal cell. In situ hybridization shows that ZmSPL10, ZmSPL14 and their target genes SPI1, ZmPIN1b, ZmARF28 and ZmWOX3A are preferentially expressed in the precursor cells of stigmatic papillae. Moreover, ZmSPL10, ZmSPL14 and ZmSPL26 directly bind to the promoters of SPI1, ZmPIN1b, ZmARF28 and ZmWOX3A and promote their expression. Further, Zmwox3a knockout mutants display severe defects in stigmatic papilla formation and reduced seed setting. Collectively, our results demonstrate that ZmSPL10, ZmSPL14 and ZmSPL26 act together to promote stigmatic papilla development through regulating auxin signaling and ZmWOX3A expression.

A Robust Medical Image Fusion Based on Synthetic Focusing Degree Criterion and Special Kernel Set for Clinical Diagnosis

A Robust Medical Image Fusion Based on Synthetic Focusing Degree Criterion and Special Kernel Set for Clinical Diagnosis

Pre-silking water deficit in maize induced kernel loss through impaired silk growth and ovary carbohydrate dynamics.

Both carbon limitation and developmentally driven kernel failure occur in the apical region of maize (Zea mays L.) ears. Failed kernel development in the basal and middle regions of the ear often is neglected because their spaces usually are occupied by adjacent ovaries at harvest. We tested the spatial distribution of kernel losses and potential underlying reasons, from perspectives of silk elongation and carbohydrate dynamics, when maize experienced water deficit during silk elongation. Kernel loss was distributed along the length of the ear regardless of water availability, with the highest kernel set in the middle region and a gradual reduction toward the apical and basal ends. Water deficit limited silk elongation in a manner inverse to the temporal pattern of silk initiation, more strongly in the apical and basal regions of the ear than in the middle region. The limited recovery of silk elongation, especially at the apical and basal regions following rescue irrigation was probably due to water potentials below the threshold for elongation and lower growth rates of the associated ovaries. While sugar concentrations increased or did not respond to water deficit in ovaries and silks, the calculated sugar flux into the developing ovaries was impaired and diverged among ovaries at different positions under water deficit. Water deficit resulted in 58% kernel loss, 68% of which was attributable to arrested silks within husks caused by lower water potentials and 32% to ovaries with emerged silks possibly due to impaired carbohydrate metabolism.

Drought stress during maize flowering may cause kernel abortion by inhibition of plasma membrane H+‐ATPase activity

AbstractBackgroundDrought stress during flowering of maize (Zea mays L.) frequently results in decreased kernel setting, leading to grain yield depressions. Plasma membrane (PM) H+‐ATPase was identified as a key enzyme responsible for supply of assimilates to the developing maize kernels shortly after pollination. The activity of this enzyme was strongly inhibited under salt stress, pointing to an involvement in kernel abortion.AimsThis study aimed to determine whether also drought stress causes inhibition of PM H+‐ATPase in developing maize kernels shortly after pollination, leading to diminished hexose uptake and finally kernel abortion. The key questions are as follows: What are the limiting factors for grain yield production of maize plants facing drought? Are physiologically relevant parameters, quantified at flowering, reflected by yield determinants at maturity?MethodsMaize plants were cultivated using the container technique, and drought stress was imposed during 3 weeks bracketing flowering compared to well‐watered conditions throughout the entire growth period. The developing kernels were harvested 2 days after pollination, and PM vesicles were isolated and purified using two‐phase partitioning.ResultsWater deficit caused a significant decrease in grain yield at maturity (−35%), which was determined by a reduced kernel number (−42%). Source limitation in the developing kernels under stress could be excluded. Acid invertase activity was unaffected by water deficit. Hexose availability was also no limiting factor for kernel setting and development. However, Vmax of in vitro hydrolytic activity of PM H+‐ATPase was significantly decreased in the developing maize kernels under drought stress and the maximal pH gradient at the PM was also significantly reduced. The observed inhibiting effects on PM H+‐ATPase were mainly of quantitative nature, as a lower number of proton pumps was present in the kernel PM. Qualitative changes of the enzyme (activation energy Ea, Michaelis constant Km) due to drought were not observed.ConclusionsThe lower pH gradient probably decreased the proton‐driven transport of hexoses by carriers into the cytosol of the kernel cells, leading to kernel starvation and eventually contributing to kernel abortion.

Neutrosophic Binary Separation Axioms associated Neutrosophic Binary Kernel Set

The primary purpose of this report is to present an idea that is considered one of the main ideas in neutrosophic binary topological spaces. We plead them the axioms of neutrosophic binary separation axioms associated in the neutrosophic binary kernel. Also we studied its characteristics and the relationships between these new neutrosophic binary separation axioms and their relationships with some other properties.

Infrared cluster target detection based on Facet kernel and adaptive distance regularization level set

The acquisition of high signal-to-noise ratio (SNR) detection results for targets in infrared images is of great significance for early warning detection and precision guidance. To address the issue of detecting cluster targets in complex backgrounds, a detection method combining the Facet kernel and adaptive distance regularization level set evolution (ADRLSE) is proposed. Firstly, local order-statistics and mean filters are applied to remove high-brightness noise and smooth the image. Subsequently, the Facet kernel filter is applied to enhance the target while suppress the background. Candidate target pixels are obtained through adaptive threshold segmentation. Additionally, based on the edge characteristics of cluster targets, a novel edge detection function is proposed that adaptively determines the parameters using the contour volume (CV) and peak signal-to-noise ratio (PSNR) as references. The enhanced mapping of the target region is obtained by extracting the cluster target contour using ADRLSE. The ADRLSE map is weighted using the target map obtained from the Facet kernel to further enhance the target, and the target is detected through thresholding operations. Finally, the separation result of a single target within a cluster is obtained through a novel adaptive k-means clustering algorithm. Simulation experiments demonstrate that our method has high detection accuracy for cluster targets and can accomplish detection tasks such as cluster contour extraction and single target division.

Flexible Lévy-Based Models for Time Series of Count Data with Zero-Inflation, Overdispersion, and Heavy Tails

The explosion of time series count data with diverse characteristics and features in recent years has led to a proliferation of new analysis models and methods. Significant efforts have been devoted to achieving flexibility capable of handling complex dependence structures, capturing multiple distributional characteristics simultaneously, and addressing nonstationary patterns such as trends, seasonality, or change points. However, it remains a challenge when considering them in the context of long-range dependence. The Lévy-based modeling framework offers a promising tool to meet the requirements of modern data analysis. It enables the modeling of both short-range and long-range serial correlation structures by selecting the kernel set accordingly and accommodates various marginal distributions within the class of infinitely divisible laws. We propose an extension of the basic stationary framework to capture additional marginal properties, such as heavy-tailedness, in both short-term and long-term dependencies, as well as overdispersion and zero inflation in simultaneous modeling. Statistical inference is based on composite pairwise likelihood. The model’s flexibility is illustrated through applications to rainfall data in Guinea from 2008 to 2023, and the number of NSF funding awarded to academic institutions. The proposed model demonstrates remarkable flexibility and versatility, capable of simultaneously capturing overdispersion, zero inflation, and heavy-tailedness in count time series data.

Ear reproductive development components associated with kernel set in maize: Breeding effects under contrasting environments

Context or problemKernel number per ear (KNE) is the main driver of improved grain yield per ear (GYE) in maize (Zea mays L.). Under non-limiting conditions, KNE responded to variations in ear developmental traits such as the number of completely developed florets (CDFE) and exposed silks per ear (ESE), which have been positively affected by breeding. This trend needs to be assessed under common abiotic constraints that usually affect rainfed maize production. Objective or research questionThe aim of the current research was to study breeding effects on maize grain yield and its associated reproductive developmental traits as well as the sources of loss of kernel number under a wide range of growing conditions per plant. MethodsField experiments were conducted at two sites of the Central Pampas of Argentina, where hybrids of different year of release (from 1980 to 2012) were evaluated under 10 managed environments generated by the combination of (i) three growing seasons (2015–16: S1; 2017–18: S2; 2018–19: S3), (ii) two nitrogen rates (N0: no nitrogen added; N200: 200 kg N ha-1), and (iii) two stand densities in S1 and S2 (9 and 12 plants m-2). Plant biomass at R1 was used as a classifier of pre-flowering growth conditions (LOW, MID-LOW, MID-HIGH, and HIGH environments). ResultsAlthough modern hybrids always presented more GYE and KNE than old hybrids, the reproductive developmental processes behind this response were different depending on the environment. Under HIGH environments, the KNE improvement (4.53 kernels year-1) was the result of a greater CDFE (7.55 florets year-1) and more ESE (6.01 silks year-1). Under LOW environments, the genetic progress in KNE (2.41 kernels year-1) was associated with a reduced kernel abortion (−0.48 % year-1). Non-significant trend with year of release was observed in spikelet growth rate in all groups of evaluated environments, however, there was a significant decrease in the residual effect of the relationship between spikelet growth rate during the critical period and kernel abortion under LOW environments. ConclusionsTraits responsible for improved grain yield differed across environments because the relative importance of the determinant processes of kernel set vary across them. Under low-yielding environments, decreased kernel abortion is more beneficial than increased potential ear size (i.e., florets per ear) to enhance kernel number and grain yield. Implications or significanceThis finding highlights the relevance of focusing on processes conducive to kernel abortion under stress-prone environments to improve maize grain yield.

Securing maize reproductive success under drought stress by harnessing CO2 fertilization for greater productivity.

Securing maize grain yield is crucial to meet food and energy needs for the future growing population, especially under frequent drought events and elevated CO2 (eCO2) due to climate change. To maximize the kernel setting rate under drought stress is a key strategy in battling against the negative impacts. Firstly, we summarize the major limitations to leaf source and kernel sink in maize under drought stress, and identified that loss in grain yield is mainly attributed to reduced kernel set. Reproductive drought tolerance can be realized by collective contribution with a greater assimilate import into ear, more available sugars for ovary and silk use, and higher capacity to remobilize assimilate reserve. As such, utilization of CO2 fertilization by improved photosynthesis and greater reserve remobilization is a key strategy for coping with drought stress under climate change condition. We propose that optimizing planting methods and mining natural genetic variation still need to be done continuously, meanwhile, by virtue of advanced genetic engineering and plant phenomics tools, the breeding program of higher photosynthetic efficiency maize varieties adapted to eCO2 can be accelerated. Consequently, stabilizing maize production under drought stress can be achieved by securing reproductive success by harnessing CO2 fertilization.

Coordinated regulation of sucrose and lignin metabolism for arrested silk elongation under drought stress in maize

The delay in silking due to drought stress generates a greater anthesis-silking interval in maize, causing compromised kernel set and thus catastrophic loss of grain yield, which is primarily attributed to the lower silk elongation. However, few studies are done in the understanding of the physiochemical and molecular mechanism that regulate silk elongation in response to drought stress. Thus, a field experiment was conducted to examine silk transcriptomic and metabolomic responses to drought stress using the maize cultivar of AN591. A total of 757 differently expressed genes (DEGs) and 80 differently expressed metabolites (DEMs) were identified in maize silk grown in the drought stress treatment compared to the control treatment. These DEGs and DEMs were enriched in 191 and 36 Kyoto Encyclopedia of genes and genomes (KEGG) pathways, respectively, most being associated with primary and secondary metabolism. Starch and sucrose metabolism (SSM) pathway in primary metabolism and phenylpropanoid biosynthesis (PB) pathway in secondary metabolism were identified in common in both transcriptome and metabolome. Most of DEGs in the SSM pathway were increased in expression, while the DEM were decreased in expression. Conversely, most of DEGs in the PB pathway were decreased in expression, while DEMs were increased in expression. This could be explained by the negative correlation between DEGs and DEMs. In the SSM pathway, there was a significant increase in sucrose content, but a significant decrease in fructose and glucose in response to drought stress, which was attributed to the decreased activities in cell wall invertase (CWIN), vacuolar invertase (VIN), and sucrose synthase (SuSy). Silk lignin content in PB pathway was in a significant decrease in drought treatment compared to the control, due to the decreased activity of peroxidase (POD) that is the key enzyme in the final stage of lignin biosynthesis. In conclusion, this study identified key genes or metabolites involved in sucrose and lignin metabolism which appears to be regulating silk elongation response to drought stress.

Heat Stress Effects on Leaf Physiological Performances, Vegetative Growth and Grain Yield of Grain Corn (Zea mays L.)

The impact of climate change on agricultural production will be most pronounced in tropical and subtropical regions, with numerous climate modeling studies predicting more occurrences of heat waves in the future. Elevated temperatures resulting from global warming pose a significant threat to the agricultural sector, as warmer conditions can hinder plant growth and development, leading to reduced crop yields or even crop failure under extreme circumstances. Therefore, this study aimed to determine the effects of ambient, non-heated (30 °C) and heat stress conditions (38 °C) on the plant physiological responses, growth and yield of grain corn during both the vegetative and reproductive stages. The results demonstrated that exposure to heat stress for 7 days significantly impacted the physiological performance of the plants, resulting in a substantial 46.9% reduction in net photosynthetic rate. However, prolonged exposure to heat for 28 days caused even more severe effects, with a 72.5% reduction in net photosynthetic rate. Although the effects of heat stress on vegetative growth were not apparent after 7 days, the plants exhibited severe damage after 28 days of heat stress treatment. During the flowering stage, heat stress led to significant reduction in kernel set, total kernel number, and grain weight of grain corn by 45%, 41%, and 46%, respectively. Poor and scattered kernel set on cobs during the heat stress treatment at the anthesis period indicated damage to pollen grains, failed pollination, and fertilization. These findings highlight the vulnerability of grain corn varieties cultivated in Malaysia to the negative impacts of heat stress, leading to potential losses in production yield.

Does the plant growth regulator paclobutrazol enhance root growth of maize exposed to drought stress during flowering?

AbstractDue to climate change, crop production will increasingly be affected by water limitation, causing remarkable decreases in grain yields of cereals. Plant growth regulators such as paclobutrazol (PAC) have been shown to protect plants from detrimental impacts of drought stress, and improvement of root growth and antioxidant activity were identified as main reasons for their positive effect. A container experiment was conducted with two maize (Zea mays L.) cultivars, Galactus and Fabregas, to investigate how PAC application affects root growth and grain yield under stress conditions. At growth stage V8, the plants were treated once with PAC (0, 2, or 3 mg PAC per plant), and concomitantly reduction in soil water content commenced until 30–35% of the maximum water‐holding capacity (WHC) was achieved. The plants were exposed to this drought condition for three weeks during flowering as the critical period for kernel setting. Both factors, PAC application and drought stress, caused decreases in plant height, whereas total leaf area was unchanged and transpiration rate was significantly reduced by water limitation only. Flowering was almost unaffected by PAC treatment; yet, drought stress significantly delayed start of silking. The straw yield was decreased due to PAC and drought stress, and an improvement of the harvest index was obtained for drought‐stressed Galactus plants with PAC application. Grain yield was unaffected by PAC application, whereas drought stress caused significant decreases by 15% on average of both cultivars. The kernel number of drought‐stressed Galactus plants was increased after PAC treatment, but concurrently smaller kernels were produced. Water limitation generally decreased kernel number. Drought‐stressed Fabregas plants consumed less water after PAC treatment, resulting in significant improvements of water‐use efficiency (WUEgrain) during silking and thus most likely alleviating stress intensity. For both cultivars, PAC treatment and water limitation showed almost no significant impact on root dry matter, root length density, and root surface area, either determined for different soil layers down to 80 cm or on a per‐plant basis. It is concluded that grain yield performance of maize plants, exposed to water limitation during flowering, was not source‐limited but sink‐limited. Consequently, even if PAC can cause improvement of antioxidant activity and photosynthesis, due to sufficient availability of assimilates in the maize kernels a positive effect on grain yield is improbable. Considering source–sink relationships during flowering and kernel set, enhanced root growth due to PAC treatment did apparently not occur.

Enhancing maize grain yield under salt‐affected field conditions using salt‐resistant maize hybrids and higher planting density

AbstractBackgroundIn arid and semiarid countries, grain yield of maize is increasingly impaired by soil salinity. Beside soil amelioration, the development of salt‐resistant cultivars is a possibility to enhance crop yield on salt‐affected soils.AimsThis study aimed at testing yield performance in the field of salt‐resistant maize hybrids on a salt‐affected soil. In addition, planting density was optimized under the saline conditions.MethodsFour salt‐resistant maize hybrids (Zea mays L. SR‐05, SR‐12, SR‐15, and SR‐16) were grown under control (EC = 2.0–2.5 dS m−1) and saline (EC = 10.0–12.0 dS m−1) field conditions and compared to the salt‐sensitive maize cv. Pioneer‐3906. Planting density (5, 8, or 11 plants m−2) was optimized for saline soil conditions for SR‐12 and the local hybrid EV‐78.ResultsYield of Pioneer‐3906 was significantly reduced under salinity because of inhibited kernel setting, whereas the SR hybrids showed no decrease in grain yield. Based on grain yield, the optimum planting density was 8 plants m−2 with no further increase with 11 plants m−2. In contrast to SR‐12, for cv. EV‐78 no increase of harvest index with 8 relative to 5 plants m−2 was observed.ConclusionsVegetative growth of Pioneer‐3906 and the SR hybrids was decreased due to Phase‐I effects but neither due to water deficiency nor ion toxicity. The experiment corroborated the salt resistance of the SR hybrids under field conditions. Under saline conditions, optimum planting density of salt‐resistant cultivars may be higher than under nonsaline conditions when sufficient water supply by artificial irrigation is guaranteed.

Effect of Chest Computed Tomography Kernel Use on Emphysema Score in Severe Chronic Obstructive Pulmonary Disease Patients Evaluated for Lung Volume Reduction

Background: Chest computed tomography (CT) emphysema quantification is a vital diagnostic tool in patient evaluation for bronchoscopic lung volume reduction (BLVR). Smooth kernels for CT image reconstruction are generally recommended for quantitative analyses. This recommendation is not always followed, which may affect quantification of emphysema extent and eventually, treatment decisions. Objective: The main goal is to demonstrate the influence of CT reconstruction kernels on emphysema quantification in patients with severe COPD, considered for BLVR. Methods: Chest CT scans were acquired with one multi-detector CT system and reconstructed using three different kernels: smooth, medium smooth, and sharp. Other parameters were kept constant. Emphysema scores (ESs), meaning the percentage of voxels below −950 Hounsfield units, were calculated and compared to the smooth reference kernel using paired t tests. Bland-Altman plots were made to assess the biases and limits of agreement between kernels. Results: Ninety-eight COPD patient CT scans were analyzed. The sharp kernel had a systematic bias of 6.2% and limits of agreement of 16.6% to −4.2% compared to the smooth kernel. The medium smooth kernel had a systematic bias of 5.7% and limits of agreement of 9.2% and 2.2% compared to the smooth kernel. The ES differed, for a single patient, up to 18% for different kernels. Conclusions: Chest CT kernel reconstruction can lead to a significant difference in emphysema severity quantification. This may cause invalid treatment selection in COPD patients evaluated for BLVR. Standardization of a smooth CT kernel setting and/or normalization to a standard kernel is strongly recommended.

Some Results on Fixed Sets and Kernel Sets of Permuting \(n\)-\((f ,g)\)-derivation of Lattices

In this paper, we introduce a multivariate fixed set, a partial fixed set, a multivariate kernel set and a partial kernel set of permuting \(n\)-\((f , g)\)-derivation in lattices, and investigate some related properties. We also give some conditions under which these sets are sublattices and ideals.

Applications of Nano Penta (N_P) Separation axioms using N_P_ open sets

The main goal of this paper is to use the concept _ open sets to present new classes of separation axioms in _topological spaces. Those new classes are _spaces, =0,1,2. We have studied some basic properties of these spaces. We also discussed the relationship between _spaces and Nano separation axioms spaces), =0,1,2. Furthermore the paper deals with the relationship between the separation axioms throughout kernel set associated with the closed set which used to prove some theorems related to it. The hereditary and topological properties were also discussed.

Sub-surface plastic mulching reduced evaporation during the fallow season and increased spring maize yield in the North China Plain

Drought stress is the main limiting factor to spring maize production in the North China Plain (NCP). However, amounts of soil water and precipitation evaporate unproductively during the long fallow season before sowing spring maize. Therefore, sub-surface plastic mulching (PM) and straw mulching (SM) were conducted to reduce evaporation following the harvest of the previous season’s crop in this three-year study. PM resulted in less water evaporation during the fallow season compared with SM and the control without mulching (CK). Thus, more water was stored in the soil under PM. Similarly, evapotranspiration from sowing to the sixth-leaf stage (V6) in CK and SM was increased by 18.5–43.1% compared with PM. Conversely, evapotranspiration of PM was higher than that of CK and SM during the V6-silking period. During the grain filling stage, there was no significant difference in evapotranspiration among treatments owing to abundant rainfall. Correlation analysis indicated that excessive soil water used during the fallow season and seedling stage exacerbated the yield reduction. However, more water employed from V6 to silking increased yield. PM increased soil temperature during the seedling stage, thus accelerating spring maize growth, but without significantly affecting grain yield. PM could increase leaf area index, thus improving the biomass accumulation. Finally, the kernel setting rate was elevated under PM compared to CK and SM. Grain yield, water use efficiency (WUE), and annual WUE of PM were increased by 9.3–15.2%, 8.7–15.6%, and 16.7–25.8% relative to CK and SM, respectively. In conclusion, sub-surface plastic mulching stored water for spring maize growth and development, and enhanced water availability during the critical stage of water demand, thus increasing grain yield and WUE and reducing annual evapotranspiration.

Reduced stem nonstructural carbohydrates caused by plant growth retardant had adverse effects on maize yield under low density.

Enhancing maize lodging resistance with plant growth retardants (PGRs) is common in maize production. However, the underlying mechanisms of yield formation as affected by PGRs are still poorly understood. A field experiment contained PGR application (a mixture of ethephon and cycocel, EC) with normal (T1) and double (T2) doses and water control (CK) was conducted at four maize plant densities (4.5, 6.0, 7.5, and 9.0 plants m-2) in 2020 and 2021. In this two-year study, the grain yield and kernel number per ear (KNE) of EC treatments were reduced by 4.8-9.0% and 3.3-12.2%, respectively, compared with CK under densities of 4.5, 6.0, and 7.5 plants m-2 without lodging. However, under the density of 9.0 plants m-2, EC treatments had no pronounced effects on grain yield and yield components. Across all densities, EC significantly decreased the leaf area index (LAI), and the lowest LAI was recorded in T2. The concentrations of nonstructural carbohydrates (NSCs; starch and soluble sugar) in the stem were significantly decreased by 9.9-10.2% in T2 averaged all densities. The sucrose and starch concentrations in grains also declined in the EC treatments. The key enzymes (cell wall acid invertase, sucrose synthase, and adenosine diphosphate pyrophosphorylase) and grain polyamine concentrations showed a slight downward trend under EC treatments compared to CK. NSCs in stems and grains, kernel enzyme activities, and polyamines in grains presented significant positive correlations with KNE. Additionally, structural carbohydrate (SC; including cellulose, hemicellulose, and lignin) concentrations in stems were improved with enhanced lodging resistance by spraying EC. Significant negative relationships were observed between SC with kernel number m-2 (KNM) and yield, suggesting that improved SC in stems might affect the availability of NSCs for kernel set. Although the lowest kernel weight and KNE were obtained at 9.0 plant m-2, relatively high LAI still ensured high KNM and high yield. Collectively, EC treatment increased SC in stems, enhanced lodging resistance of maize and reduced NSC availability for kernels, ultimately presenting adverse effects on maize kernel number and yield under relative low density.

Physiological mechanisms underlying genetic improvement in sink establishment and plant‐to‐plant variability in maize

Genetic improvement in maize (Zea mays L.) grain yield is associated with improvements in dry matter accumulation during the grain-filling period and the ability to maintain partitioning to the grain (i.e., harvest index) when grown at higher plant population densities. Although several attributes have been identified that lead to improved dry matter accumulation during the grain-filling period, the attributes that have enabled the maintenance of harvest index at higher plant population densities remain elusive. Using the Ontario ERA hybrids that represent five eras of genetic improvement in Ontario, we examined genetic improvement in several attributes associated with sink establishment and partitioning to the grain. We show that the number of florets on an ear initial is not influenced by plant density, nor is there any evidence of a genetic improvement in floret number. There has been genetic improvement, however, in the ability to support kernel set at a lower threshold level of dry matter accumulation and to more efficiently set kernels at lower dry matter accumulation levels, such as those experienced at higher plant densities. Genetic improvement is evident for reduced plant-to-plant variability for dry matter accumulation, grain yield, kernel number, and plant growth rate around silking, but most notably for grain yield. Finally, we show that genetic improvement in reduced plant-to-plant variability for grain yield is the result of the lower threshold dry matter levels required for seed set and the improved resource utilization, which has led to greater stability of individual plant performance in a stand.

Detection of COVID-19 Cases from Chest X-Rays using Deep Learning Feature Extractor and Multilevel Voting Classifier

Purpose: During the current pandemic scientists, researchers, and health professionals across the globe are in search of new technological methods for tackling COVID-19. The magnificent performance reported by machine learning and deep learning methods in the previous epidemic has encouraged researchers to develop systems with these methods to diagnose COVID-19. Methods: In this paper, an ensemble-based multi-level voting model is proposed to diagnose COVID-19 from chest x-rays. The multi-level voting model proposed in this paper is built using four machine learning algorithms namely Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM) with a linear kernel, and K-Nearest Neighbor (KNN). These algorithms are trained with features extracted using the ResNet50 deep learning model before merging them to form the voting model. In this work, voting is performed at two levels, at level 1 these four algorithms are grouped into 2 sets consisting of two algorithms each (set 1 — SVM with linear kernel and LR and set 2 — RF and KNN) and intra set hard voting is performed. At level 2 these two sets are merged using hard voting to form the proposed model. Results: The proposed multilevel voting model outperformed all the machine learning algorithms, pre-trained models, and other proposed works with an accuracy of 100% and specificity of 100%. Conclusion: The proposed model helps for the faster diagnosis of COVID-19 across the globe.