Fine mapping of the powdery mildew resistance gene PmXQ-0508 in bread wheat

In a wheat breeding line XQ-0508 showing consistent resistance to powdery mildew disease,a recessive gene,designated PmXQ-0508,was identified and mapped to a distal region on chromosome arm 2BS.Of three resistance-associated genes in this region,one encoding a protein kinase was selected as the primary candidate for PmXQ-0508.Ten closely linked DNA markers developed in the study could be used for marker-assisted selection for powdery-mildew resistance in breeding programs.

Improving model performance in mapping cropland soil organic matter using time-series remote sensing data

Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management policies.As a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different scales.However,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance.To overcome this limitation,this study systematically assessed a framework of“information extractionfeature selection-model averaging”for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in 2021.The results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R^(2):0.48 to 0.53)without having obviously negative impacts on uncertainty.Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R^(2)of random forest from 0.44 to 0.48 and the R^(2)of extreme gradient boosting from 0.37to 0.43.Forward recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(<200),whereas Boruta is recommended when there are many environmental covariates(>500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty.When the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final predictions.Given the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales,so this approach can provide more reliable references for soil conservation policy-making.

Selective Mapping Scheme for Universal Filtered Multicarrier

The next step in mobile communication technology,known as 5G,is set to go live in a number of countries in the near future.New wireless applica-tions have high data rates and mobility requirements,which have posed a chal-lenge to mobile communication technology researchers and designers.5G systems could benefit from the Universal Filtered Multicarrier(UFMC).UFMC is an alternate waveform to orthogonal frequency-division multiplexing(OFDM),infiltering process is performed for a sub-band of subcarriers rather than the entire band of subcarriers Inter Carrier Interference(ICI)between neighbouring users is reduced via the sub-bandfiltering process,which reduces out-of-band emissions.However,the UFMC system has a high Peak-to-Average Power Ratio(PAPR),which limits its capabilities.Metaheuristic optimization based Selective mapping(SLM)is used in this paper to optimise the UFMC-PAPR.Based on the cognitive behaviour of crows,the research study suggests an innovative metaheuristic opti-mization known as Crow Search Algorithm(CSA)for SLM optimization.Com-pared to the standard UFMC,SLM-UFMC system,and SLM-UFMC with conventional metaheuristic optimization techniques,the suggested technique sig-nificantly reduces PAPR.For the UFMC system,the suggested approach has a very low Bit Error Rate(BER).

Fine mapping and marker-assisted selection (MAS) of a low glutelin content gene in rice

Rice with low glutelin content is suitable as functional food for patients affected with diabetes and kidney failure. The fine mapping of the gene(s) responsible for low glutelin content will provide information regarding the distribution of glutelin related genes in rice genome and will generate markers for the selection of low glutelin rice varieties. Following an SDS-PAGE screen of rice germplasm from Taihu Valley of China, Japonica selection W3660 is identified to be a novel mutant characterized with low glutelin content. For fine mapping the mutant gene for low glutelin content, F2 and F3 populations were derived from a cross between W3660 and Jingrennuo. SDS-PAGE analysis of the total endosperm protein showed that the low glutelin content trait was controlled by a single dominant nuclear gene. Genetic mapping, using SSRs, located this gene to chromosome 2, in the region between SSR2-001/SSR2-004 and RM1358. The dis- tances of the two markers to the target gene were 1.1 cM and 3.8 cM respectively. By semi-quantitative RT-PCR analysis, the transcripts of GluB4/GluB5 genes located within the region do not change. However, GluB5 gene located proximal to SSR2-001/SSR2-004 was specifically reduced. SSR profiles of seven Japonica varieties were compared with that of W3660 for loci in the relevant genetic region. The markers SSR2-004 and RM1358 were used for marker- assisted selection. The selection efficiencies of SSR2-004 and RM1358 were 96.8% and 92.7% respectively. This provides a standard starting point for the breeding of low glutelin content rice varieties in China.

Combining rough set theory and instance selection in ontology mapping

This paper presents a novel ontology mapping approach based on rough set theory and instance selection.In this appoach the construction approach of a rough set-based inference instance base in which the instance selection(involving similarity distance,clustering set and redundancy degree)and discernibility matrix-based feature reduction are introduced respectively;and an ontology mapping approach based on multi-dimensional attribute value joint distribution is proposed.The core of this mapping aI overlapping of the inference instance space.Only valuable instances and important attributes can be selected into the ontology mapping based on the multi-dimensional attribute value joint distribution,so the sequently mapping efficiency is improved.The time complexity of the discernibility matrix-based method and the accuracy of the mapping approach are evaluated by an application example and a series of analyses and comparisons.

CONTINUOUS SELECTION THEOREMS FOR FAN-BROWDER MAPPINGS IN TOPOLOGICAL SPACES AND THEIR APPLICATIONS

The concept of Fan-Browder mappings was first introduced in topological spaces without any convex structure. Then a new continuous selection theorem was obtained for the Fan-Browder mapping with range in a topological space without any convex structure and noncompact domain. As applications, some fixed point theorems, coincidence theorems and a nonempty intersection theorem were given. Both the new concepts and results unify and extend many known results in recent literature.

Genome Mapping to Enhance Efficient Marker-Assisted Selection and Breeding of the Oil Palm (<i>Elaeis guineensis</i>Jacq.)

The oil palm (Elaeis guineensis Jacq.) is one of the major cultivated crops among the economically important palm species. It is cultivated mainly for its edible oil. For a perennial crop like oil palm, the use of Marker Assisted Selection (MAS) techniques helps to reduce the breeding cycle and improve the economic products. Genetic and physical maps are important for sequencing experiments since they show the exact positions of genes and other distinctive features in the chromosomal DNA. This review focuses on the role of genome mapping in oil palm breeding. It assesses the role of genome mapping in oil palm breeding and discusses the major factors affecting such mapping. Generating a high-density map governed by several factors, for instance, marker type, marker density, number of mapped population, and software used are the major issues treated. The general conclusion is that genome mapping is pivotal in the construction of a genetic linkage map. It helps to detect QTL and identify genes that control quantitative traits in oil palm. In perspective, the use of high-density molecular markers with a large number of markers, a large number mapping population, and up-to-date software is necessary for oil palm genome mapping.

Short-span interleaving based selected mapping

A novel interleaving based selected mapping （SLM） scheme to depress the relatively high peak power of transmit signals in multicarrier communications is proposed. In the scheme, a group of bit-level interleavers spanning only a few bits are used to produce multiple sequences representing the same information, and one of the sequences resulting in the lowest peak-to-average power ratio （PAPR） is selected for transmission. The implementation of the scheme including the structure of the short-span interleaver is illustrated. The performance of this PAPR reduction scheme is investigated by simulations. This scheme exhibits a good PAPR reduction performance, and for signals of high level modulation, such as 16QAM and 64QAM, it approaches the best performance of all SLM schemes. Compared to the conventional interleaving SLM, this short-span interleaving SLM results in a very short time delay, requires very few register units for buffering, and can be easily implemented by hardware.

An integrated method of selecting environmental covariates for predictive soil depth mapping

Environmental covariates are the basis of predictive soil mapping.Their selection determines the performance of soil mapping to a great extent,especially in cases where the number of soil samples is limited but soil spatial heterogeneity is high.In this study,we proposed an integrated method to select environmental covariates for predictive soil depth mapping.First,candidate variables that may influence the development of soil depth were selected based on pedogenetic knowledge.Second,three conventional methods(Pearson correlation analysis(PsCA),generalized additive models(GAMs),and Random Forest(RF))were used to generate optimal combinations of environmental covariates.Finally,three optimal combinations were integrated to produce a final combination based on the importance and occurrence frequency of each environmental covariate.We tested this method for soil depth mapping in the upper reaches of the Heihe River Basin in Northwest China.A total of 129 soil sampling sites were collected using a representative sampling strategy,and RF and support vector machine(SVM)models were used to map soil depth.The results showed that compared to the set of environmental covariates selected by the three conventional selection methods,the set of environmental covariates selected by the proposed method achieved higher mapping accuracy.The combination from the proposed method obtained a root mean square error(RMSE)of 11.88 cm,which was 2.25–7.64 cm lower than the other methods,and an R^2 value of 0.76,which was 0.08–0.26 higher than the other methods.The results suggest that our method can be used as an alternative to the conventional methods for soil depth mapping and may also be effective for mapping other soil properties.

EXISTENCE PROBLEMS OF ADDITIVE SELECTION MAPS FOR ANOTHER TYPE SUBADDITIVE SET-VALUED MAP

In this paper, we consider the following subadditive set-valued map F ： X→P0（Y） ：where r and s are two natural numbers. And we discuss the existence and unique problem of additive selection maps for the above set-valued map.

The Unique Existence of Additive Selection Maps for a Weak Subadditive Set-valued Map

In this paper, we discuss the unique existent problems of a additive selection map for the following map F:X→P0(Y):F(r∑i=1αixi)■r∑i=1αiF(xi),■xi∈K,i=1, ··· , r,where αi and αi (i = 1, ··· , r) are all non-negative real numbers.

An Adaptive Sequential Replacement Method for Variable Selection in Linear Regression Analysis

With the rapid development of DNA technologies, high throughput genomic data have become a powerful leverage to locate desirable genetic loci associated with traits of importance in various crop species. However, current genetic association mapping analyses are focused on identifying individual QTLs. This study aimed to identify a set of QTLs or genetic markers, which can capture genetic variability for marker-assisted selection. Selecting a set with k loci that can maximize genetic variation out of high throughput genomic data is a challenging issue. In this study, we proposed an adaptive sequential replacement (ASR) method, which is considered a variant of the sequential replacement (SR) method. Through Monte Carlo simulation and comparing with four other selection methods: exhaustive, SR method, forward, and backward methods we found that the ASR method sustains consistent and repeatable results comparable to the exhaustive method with much reduced computational intensity.

An Improved Selective Mapping Method for PAPR Reduction in OFDM/OQAM System

Orthogonal frequency division multiplex/offset QAM (OFDM/OQAM) has been proven to be a promising multi-carrier modulation (MCM) technique for the transmission of signals over multipath fading channels. However, OFDM/OQAM has also the intrinsic disadvantage of high peak-to-average-power ratio (PAPR) that should be alleviated. In this paper, a novel selective mapping (SLM) method is proposed for OFDM/OQAM system. Since the pulse shape may cover a few OFDM symbols, the basic principle of the proposed method is to apply the SLM method in the range of the most relevant OFDM symbols. Analysis and simulation results show that, compared to the existing SLM algorithms for OFDM/OQAM system, the proposed method has better PAPR performance and lower computation complexity.

Selective sampling with Gromov–Hausdorff metric:Efficient dense-shape correspondence via Confidence-based sample consensus

Background Functional mapping, despite its proven efficiency, suffers from a “chicken or egg” scenario, in that, poor spatial features lead to inadequate spectral alignment and vice versa during training, often resulting in slow convergence, high computational costs, and learning failures, particularly when small datasets are used. Methods A novel method is presented for dense-shape correspondence, whereby the spatial information transformed by neural networks is combined with the projections onto spectral maps to overcome the “chicken or egg” challenge by selectively sampling only points with high confidence in their alignment. These points then contribute to the alignment and spectral loss terms, boosting training, and accelerating convergence by a factor of five. To ensure full unsupervised learning, the Gromov–Hausdorff distance metric was used to select the points with the maximal alignment score displaying most confidence. Results The effectiveness of the proposed approach was demonstrated on several benchmark datasets, whereby results were reported as superior to those of spectral and spatial-based methods. Conclusions The proposed method provides a promising new approach to dense-shape correspondence, addressing the key challenges in the field and offering significant advantages over the current methods, including faster convergence, improved accuracy, and reduced computational costs.

An Improvement of Existence Theorems of Additive Selection Maps for Sub-additive Set-valued Maps

In this paper, we will construct a new class of subadditive set-valued maps and use Cantor theorem to prove that the set-valued map has an unique additive selection map when the set-valued map satisfies some certain conditions, and then compare the obtained result with the well-known results.

Peak-to-average power ratio reduction using selective mapping with unequal power distribution

A new approach for peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems was proposed.This approach is based on assigning powers to the different subcarriers of OFDM using an unequal power distribution strategy.In addition,a reduced complexity selective mapping (RC-SLM) scheme was proposed.The proposed scheme is based on partitioning the frequency domain symbol sequence into several sub-blocks,and then each sub-block is multiplied by different phase sequences whose length is shorter than that used in the conventional SLM scheme.Then,a kind of low complexity conversions is used to replace the IFFT blocks.The performance of the proposed RC-SLM scheme along with the new approach was studied with computer simulation.The obtained results show that the proposed RC-SLM scheme is able to achieve the lowest computational complexity when compared with other low complexity schemes proposed in the literature while at the same time improves the PAPR reduction performance by about 0.3 dB.

Genome Maps, Genetic Diversity and Marker-Assisted Selection for Soybean Improvement

Soybean [Glycine max (L.) Merr.] is a major legume used for human and livestock consumption. It has protein quality and oil contents that closely meet the dietary requirements for both humans and animals (Lusas, 2004).

Continuous Selections and Fixed Points forφ-maps with Their Applications to Section Problems

The concept of finitely continuous topological space is introduced and the basic properties of the space are given. Several continuous selection theorems and fixed point theorems for Ф-maps are established, and as applications of the above fixed point theorems, some section problems are discussed. The results generalize and improve many corresponding conclusions.

Hand segmentation from a single depth image based on histogram threshold selection and shallow CNN

Real-time hand gesture recognition technology significantly improves the user's experience for virtual reality/augmented reality(VR/AR) applications, which relies on the identification of the orientation of the hand in captured images or videos. A new three-stage pipeline approach for fast and accurate hand segmentation for the hand from a single depth image is proposed. Firstly, a depth frame is segmented into several regions by histogrambased threshold selection algorithm and by tracing the exterior boundaries of objects after thresholding. Secondly, each segmentation proposal is evaluated by a three-layers shallow convolutional neural network(CNN) to determine whether or not the boundary is associated with the hand. Finally, all hand components are merged as the hand segmentation result. Compared with algorithms based on random decision forest(RDF), the experimental results demonstrate that the approach achieves better performance with high-accuracy(88.34% mean intersection over union, mIoU) and a shorter processing time(≤8 ms).

Molecular and Physical Mapping of Powdery Mildew Resistance Genes and QTLs in Wheat: A Review

Wheat powdery mildew （Pro） is a major disease of wheat worldwide. During the past years, numerous studies have been published on molecular mapping of Pm resistance gene（s） in wheat. We summarized the relevant findings of 89 major re- sistance gene mapping studies and 25 quantitative trait loci （QTL） mapping studies. Major Pm resistance genes and QTLs were found on all wheat chromosomes, but the Pm resistance genes/QTLs were not randomly distributed on each chromosome of wheat. The summarized data showed that the A or B genome has more major Pm resistance genes than the D genome and chromosomes 1A, 2A, 2B, 5B, 5D, 6B, 7A and 7B harbor more major Pm resistance genes than the other chromosomes. For adult plant resistance （APR） genes/QTLs, B genome of wheat harbors more APR genes than A and D genomes, and chromo- somes 2A, 4A, 5A, 1B, 2B, 3B, 5B, 6B, 7B, 2D, 5D and 7D harbor more Pm resistance QTLs than the other chromosomes, suggesting that A genome except 1A, 3A and 6A, B genome except 4B, D genome except 1D, 3D, 4D, and 6D play an impor- tant role in wheat combating against powdery mildew. Furthermore, Pm resistance genes are derived from wheat and its rela- tives, which suggested that the resistance sources are diverse and Pm resistance genes are diverse and useful in combating against the powdery mildew isolates. In this review, four APR genes, Pm38/Lr34/Yr18/Sr57, Pm46/Lr67/Yr46/Sr55, Pm？/Lr27/Yr30/ SY2 and Pm39/Lr46/Yr29, are not only resistant to powdery mildew but also effective for rust diseases in the field, indicating that such genes are stable and useful in wheat breeding programmes. The summarized data also provide chromosome locations or linked markers for Pm resistance genes/QTLs. Markers linked to these genes can also be utilized to pyramid diverse Pm resis- tance genes/QTLs more efficiently by marker-assisted selection.