Recent advancement in speciation biology proposes that genetic differentiation across the whole genome  may occur at the beginning of a speciation process and that GD itself may accelerate the rate of speciation. The fall armyworm  has been used as a model species to study the process of speciation between diverging host-plant strains. We showed in a previous study that GD between the host-plant strains occurred at the beginning of a speciation process based on a population genomics analysis from a population in Mississippi (USA), providing empirical support for the theoretical prediction. In a recent paper, however, panmixia was reported in FAW based on the genomic analysis of 55 individuals collected from Argentina, Brazil, Kenya, Puerto Rico, and the mainland USA. If panmixia is true, the observed differentiation in Mississippi could be at most a phenomenon specific to a geographic population, rather than a status during a speciation process. In this report, we reanalyzed the resequencing data to test the existence of population structure according to host plants using different bioinformatics pipelines.ST statistics, and ancestry coefficient analysis supported genetic differentiation between strains regardless of the used bioinformatics pipelines. The strain-specific selective sweep was observed from the Z chromosome, implying the presence of strain-specific divergence selection. Z chromosome has a particularly high level of genetic differentiation between strains, while autosomes have low but significant genetic differentiation. Intriguingly, the re-sequencing dataset demonstrates the spread of Bacillus thuringiensis resistance mutations from Puerto Rico to the US mainland.Principal component analysis, FThese results show that a pair of host-plant strains in FAW experience genomic differentiation at the beginning of a speciation process, including Z chromosome divergent selection and possibly hitchhiking effect on autosomal sequences.The online version contains supplementary material available at 10.1186/s12862-022-02008-7. Speciation process is hampered by gene flow between a pair of diverging taxa in the absence of a geographic reproductive barrier . If onlyTheoretical predictions, however, show that GD may occur at the beginning of a speciation process, rather than at the end, if the diverging effect of divergent selection dominates the homogenizing effect of gene flow. For example, if a locus is targeted by a very strong divergent selection, such that a selection coefficient is higher than migration rate  or recomThe rate of GD has a non-linear relationship with the accumulated number of loci targeted by divergent selection , 9. DiveSpodoptera frugiperda, Lepidoptera: Noctuidae) is native to North and South America, while invasive FAWs populations have been reported from Africa, Asia, and Oceania since 2016 [The fall armyworm . We concluded that the combined effect of mild divergent selection may cause GD at the beginning of the speciation process even though this GD does not guarantee the completion of speciation. This GD suggests the condition for genome-wide congealing.In a previous study, we showed that GD between diverging taxa in sympatry may occur at the beginning of the speciation process , in lineRecently, Schlum et al. reported panmixia among FAW populations through the analysis of 55 samples collected from a wide range of geographic locations including Argentina, Brazil, Kenya, Puerto Rico, and the mainland USA , since tBBDUK [Bwa [Bcftools mpileup [3.1 [AdapterRemoval [owtie2 [GATK HaplotypeCaller [We re-used the dataset generated by Schlum et al. to test if population structure according to host plant strains is supported in FAW using different bioinformatics pipelines. First, we used the same methods as Schlum et al. to test if the same trend can be reproduced by performing variant calling for each individual and by merging the resulting files into one. Since they used BBDUK , Bwa [23 mpileup , and refeup [3.1  for readrRemoval ), mappinpeCaller ), and a peCaller ). This bThe resequencing data contained 42 sfC samples, eight sfR samples, three hybrid samples, and two unknown samples, identified from a single nucleotide position at the TPI exon 4 shown in Additional file We performed principal component analysis (PCA) to test population structure. In all cases of BBB3-indi, BBB3-all, AOG7, sfR was clearly separated from sfC at the second principal component Fig. , implyinST [ST than 0.067, suggesting statistically significant genetic differentiation between sfC and sfR . Statistical genetic differentiation between sfC and sfR was also observed from BBB3-all  and AOG7 , again showing that this trend is robust against used methods.We tested genetic differentiation between sfC and sfR. Pairwise Weir and Cockerham’s FST  was 0.06ST calculation in 200 kb windows. If > 90% of 200 kb windows have FST > 0, we considered that the dataset has GD, as we defined in our previous study [ST > 0. Thus, GD appears not to have occurred yet. This pattern is in contrast with the result from a population from Mississippi, from where 93.8% of 200 kb windows had FST > 0 [Then, we tested if GD is supported by Fus study . We did us study  and, conAncestry coefficient analysis from AOG7 was performed to infer the genetic relationship among the ancestry of each individual. The ancestry of sfC individuals was diverse while that of sfR individuals had a distinct ancestry from sfC in a wide range of K values Fig. . This reST calculated between sfC and sfR exhibited obvious outliers on the Z chromosomes, which was not observed from a random grouping . No random grouping out of 100 replications generated higher FST than 0.0414 on autosomes  toxin. Five causal Cry1F resistance mutations at ABCC2 gene have been reported, including GC bi-nucleotide insertion from a population in Puerto Rico [The resequencing data generated by Schlum et al.  is very rto Rico , 34 and rto Rico , as wellrto Rico . We reporto Rico . In the rto Rico , two BraA possible criticism against genomic differentiation between host-plant strains is that the observed difference between sfC and sfR might be due to genetic differentiation between a population containing the sfR samples and the rest of the populations with different geographic locations. The four individuals out of a total of eight sfR individuals were collected from Puerto Rico, Florida, and Texas, where sfC individuals were also collected. Therefore, the genomic differentiation between sfC and sfR is not likely to represent the differentiation according to geographic populations.We showed that FAW experienced genomic differentiation between host-plant strains from the resequencing data of 55 samples collected from a wide range of geographic locations including Argentina, Brazil, Kenya, Puerto Rico, and the mainland USA by Schlum et al. , regardlSince the samples used by Schlum et al.  were colhttps://bipaa.genouest.org/is/) [The resequencing fastq files generated by Schlum et al.  was downorg/is/)  using Bworg/is/) , and resorg/is/) . We did org/is/) . The resorg/is/) . If an Sorg/is/) . This reNext, variant calling was performed from all bam files simultaneously using mpileup at bcftools v1.9 . Then, wAdapterRemoval v2.1.7 [To generate the third resequencing dataset, adapters and low-quality base pairs were removed from the raw reads using l v2.1.7 . Reads wl v2.1.7  with –vel v2.1.7 . Variantl v2.1.7 . An SNP l v2.1.7 . The resST [The information of identified strain was obtained from Additional file ST  was calcST  for eachST . We usedST  for anceST . The numAdditional file 1: Figure S1. The result of principal component analysis with the information of sampled locations. Figure S2. The principal component analysis shown in Fig. Additional file 2: Table S1. The list of genes within the identified targets of sfC-specific selective sweeps.


Vitis vinifera accessions that included some table cultivars. Several synonymies, homonymies and mislabeling have also been detected. Structure analysis allowed identifying six clusters according to eco-geographic cultivation areas and one additional group including non-vinifera accessions. Diversity analysis pointed out that Spanish Mediterranean varieties are genetically closer to oriental genotypes than to European varieties typical of oceanic and continental climates. The origin of Spanish varieties is discussed in depth considering our data and previous studies. Analysis of molecular variance partition confirmed a well-structured germplasm, although differentiation among groups had a much lower effect on genetic variability than differences within groups, which are strongly related to a very high heterozygosity. A core collection that covers all allele richness is proposed. It is constituted of about 13% of total accessions, and each cluster inferred by structure analysis is represented.The IFAPA research center “Rancho de la Merced”  hosts one of the oldest and most diverse grapevine germplasm repositories in Europe, and is aimed at providing feasible solutions to deal with any agronomic trait by exploring its genetic variability and by means of association and Deoxyribonucleic Acid (DNA) editing studies. In this work, we focused on a wine and dual-use grapevine subcollection that consists of 930 accessions. Genetic analysis allowed to identify 521 unique genotypes. After comparing them with several databases, matches were found for 476 genetic profiles while the remaining 45 have not been previously described. Combination with clustering analysis suggested a total pool of 481  Vitis spp.) is a major vegetative propagated fruit crop with high socioeconomic importance worldwide [Vitis vinifera L. [Vitis International Variety Catalogue  [Vitis species. For each, VIVC attempts to compile information about denominations (prime name and synonyms), historic references, passport data, morphological traits, use, molecular markers for identification, pedigree, etc. “Microsatellites by varieties” and “microsatellites by profiles” are especially useful sections, which support cultivar identification by providing genetic profiles of the nine Genres081/GrapeGen06 nuclear SSRs (Simple Sequence Repeat) markers VVS2, VVMD5, VVMD7, VVMD25, VVMD27, VVMD28, VVMD32, VrZAG62 and VrZAG79 [Vitis vinifera subp. sylvestris and Vitis vinifera subp. vinifera grapevine groups [Germplasm repositories are a strategic resource for plant biodiversity conservation. Their efficient management and use are critical issues, especially in the case of field genebanks, of which the maintenance can be expensive. Well-managed plant collections both safeguard genetic diversity and make it available to breeders . Grapeviorldwide , with a ifera L. . For theil 2022) ) provide VrZAG79 ). SSRs c VrZAG79 . Core co VrZAG79 ,12,13,14e groups . In grapVitis accessions belonging to the “Rancho de la Merced” grape germplasm collection  have been studied by 13 nuclear microsatellites. It is the most ancient and the second biggest grape genebank in Spain and one of the most important worldwide with approximately 1800 accessions among “vinifera” and “sylvestris” grapevines, Vitis species, rootstocks (RS) and interspecific crosses (IC). Its origin dates back to the foundation of the “Granja Escuela Práctica de Agricultura Regional de Jerez de la Frontera” in 1887, as a response to phylloxera appearance in southern Spain. In order to assist the local vitiviniculture facing this major concern, in 1907, the first Spanish ampelographic station was established in Jerez and included a collection of rootstocks and grapevine cultivars [In this paper, 930 ultivars . The gerultivars . A new rultivars .Due to limited resources and practicality, replantation of the entire collection will not be done all at once, but by batches. The 930 studied accessions are planted in three plots assigned to the conservation of wine grape varieties for which replantation will be firstly achieved, since wine is largely the prevalent use for cultivated grapevine in both Spain and Andalusia . The reAccording to registration data, the 930 accessions included in this study have been received from 33 locations belonging to 10 countries. In total, 496 (53.3%) come from Spanish donors, while 247 (26.6%) donors were Italian, French or Portuguese. The rest of the countries were only represented by few accessions (between 1 and 25). In seven cases, analysis of the two replicates per accession provided mismatching genotypes, so that the final number of analyzed accessions was 937 . Accessivinifera in the VIVC, could be an IC or was used to obtain them. In fact, the two accessions whose profile matches with the putative vinifera Churrín de Janeo are denominated Híbrida and Híbrida Blanca, respectively (“híbrida” means hybrid in Spanish), and actually, their morphological appearance confirms the assignation to IC . For Noah IC . We also excluded from the vinifera pool the accession Ikawa Opale (that groups in sP3) based on NJ and VIVC information.Genotypes with an unknown utilization comprise the 45 unidentified profiles and cv. Gabriela. In total, 24 somatic mutants of 17 varieties and 2 from an interspecific cross were verified. In addition, four molecular variants  were detected for Colombard, Isabella (interspecific cross), and two accessions of Pinot Meunier. Finally, nine genotypes (including the two Pinot Meunier and four unidentified accessions) showed putative chimerism at least at one locus  showed a tendency towards a plateau. The ΔK criterion indicated K = 2 and K = 7 as the most pertinent levels of population subdivision . SubsequPlots were generated with the DISTRUCT software based on the Q-matrix consensus permuted across 10 replications for K = 2 to K = 6 using the CLUMPP software. Each accession’s genome is represented by a single vertical line, which is partitioned into colored segments in proportion to the estimated membership in the two, three, five and seven subpopulations.Data obtained by STRUCTURE were combined with information concerning the geographical origin of each variety provided in the VIVC. This info has been assumed as the best option despite some inconsistences detected (see discussion). Within individuals displaying ancestry values above the chosen threshold (Q ≥ 0.78), the sP1 comprises 73 genotypes, 44 of which are Spanish varieties mainly cultivated in the Central and Mediterranean regions, 8 varieties are Portuguese, typically cultivated in the South-Central regions, and only 4 are French . The sP2vinifera genotypes, the only three recognized vinifera varieties clustering here were admixed. The percentage of admixed accessions ranges from 36% at K = 2 to 45% at K = 7. In spite of the changes in the admixture levels, the comparison between different clustering steps showed that sP1  together with sP2  clearly differentiate already at K = 2 from sP4, sP6 and sP7 ; however, sP1 presented a lower admixture than sP2. It is noteworthy that all 73 genotypes composing sP1 were already well discriminated from K = 2 through all the other K values explored till K = 7. Subpopulations sP3  and sP5 (northern Iberian Peninsula and western France), which were mainly admixed for K = 2, got differentiated from K = 3. At K = 5, all subpopulations were discriminated except for sP4  and sP6 , which presented very high and moderate admixture levels, respectively , while those within IBER 2 and IBER 3/Q1 grouped for the most part in sP1 .STRUCTURE analyses best fit at K = 2, while DAPC analyses disclose three main subpopulations.V. vinifera genotypes. The PIC for each locus is shown in These analyses were performed using the pool of 481. Based on the M-method, 35 cultivars (core-35) were sufficient to capture all the 112 alleles occurring with a frequency more than 1%. The core-35 was then used to design the final genetic core collection retaining 100% of SSR diversity, i.e., 168 alleles. The optimal size of this core was 63 individuals (core-63); thus, 28 accessions were added at this step to retain 56 rare alleles . In bothhttps://www.mapa.gob.es/app/regVar/ResBusVariedades.aspx?id=es&TxtEspecie=VID&IDEspecie=119 (accessed on 12 April 2022)) . A list of changes will be privately suggested to JKI; in this work, Blanca Gordal has been assigned for genotype 63 ).Establishing the geographical origin and the correct prime name of a grapevine variety can become difficult due to the existence of a great number of synonyms and homonyms locally used, especially in the Mediterranean basin, as a result of the human displacements and migration through the centuries . Therefootype 63  instead For varieties Cañocazo and Malvasia di Lipari  and three are leaf morphology sports .vinifera varieties; however, based on strict botanical criteria, they are interbreeding crosses  in the same range of previous reports [orientalis [ST values were statistically consistent. Nevertheless, only the general tendency can be compared with other studies because of the different definitions, estimation methods and interpretations of FST generate some confusion in the literature [All  reports ,24,26. Iientalis , but proterature .orientalis-antasiatica in NJ, although some accessions laid within the proles pontica, ponticaoccidentalissubproles caspica and antasiatica within proles orientalis , such as Clairette, Pinot Noir, etc., conserve clear wild morphological traits [sylvestris populations that included NSW members.Myles et al.  found stenotypes ,35. In aenotypes ,36,37. Genotypes  also sugl traits ,32,35. Ml traits  showed al traits : Spanishl traits , PCA sepsylvestris. Myles et al. could not prove a lack of introgression from Spanish cultivated grapes into SSW because of the very low number of Spanish cvs. included in that study. However, De Andrés et al. [vinifera-sylvestris hybrids in Southern wild populations, which could mean gene-flow occurred in both directions . Therefore, since Eastern grapevines were introduced in Spain by Phoenician and Greeks, putative repeated hybridization and backcrossing events between both subspecies may be supposed, resulting in the reduction of the genetic diversity among them (more than in other European areas) and obtaining new domesticated forms, without discarding the possibility that some primordial domestication had occurred even in former times [Interestingly, Eastern cultivated grapes are closer to SSW than to other European s et al.  detecteder times ,39. Throer times . Despiteer times  and Hebéer times , has beeer times . To delvFinally, it is worth mentioning that Eastern genotypes’ contribution to Spanish grapevines is additionally proven by the presence of some accessions carrying the chlorotype D, such as Palomino Fino, the main wine variety in Andalusia, and Jaén Tinto and Doradilla, which are considered autochthonous of this region. Indeed, the type D chlorotype is common in wild forms eastward from Italy to the Middle East  and its Vitis vinifera.Core-35 and -63 include 7.3% and 13.1% of the total accessions of the collection, respectively, these results being in accordance with previous studies . These chttps://www.google.es/maps/place/IFAPA+Center+Rancho+la+Merced/@36.72794,6.1658487,17z/data=!3m1!4b1!4m5!3m4!1s0xd0dc3fd6b941443:0x6e5d06.16366 (accessed on 12 April 2022)). It preserves about 1800 accessions, including Vitis vinifera subp. vinifera and Vitis vinifera subp. sylvestris, other Vitis species, RS and IC . Each accession is in a subplot consisting of five biological replicates obtained by grafting scions on RS 161-49 Couderc. For most of the accessions, the following data are available: accession name, country and center that provided cuttings, year of reception, phenology , must quality , use and skin Page 7/16 color [Vitis vinifera wine grape varieties, composed of 930 subplots. Two vines out of five replicates per accession were analyzed, although in some cases, only one plant was available.The grapevine germplasm repository “Rancho de la Merced” is located at the namesake IFAPA center, occupying approximately a 3.5 ha surface area (google maps location link: Vitis databases when no match was found in VIVC: the Grapevine Collection at the FEM (ITA362), the Germplasm Repository at the “Consiglio per la Ricerca in Agricoltura e l’analisi dell’Economia Agraria-Viticoltura ed Enologia” ), The “Instituto Madrileño de Investigación y Desarrollo Rural, Agrario y Alimentario” (IMIDRA) Grapevine Germplasm Collection ), the Italian Grapevine Variety National Register ), the variety collection at Canarias Islands (http://Vitiscanarias.com/ (accessed on 12 April 2022)) and the Grapevine Variety Collection at the “Instituto de la Vid y el Vino de Castilla la Mancha” ). In addition, for the GrapeGen06 SSR set, data were coded for comparability of microsatellite profiles according to Maul et al. [Vitis Database (http://www.eu-Vitis.de/index.php (accessed on 12 April 2022)).DNA was extracted from 0.05 g of fresh young leaves using the DNeasy 96 Plant Kit  with a slight modification consisting in the addition of a pinch of polyvinylpyrrolidone PVP 40,000 to the extraction buffer. DNA yield and quality were determined by NanoDrop ND-1000 spectrophotometer . In some cases, quality was further checked in 1% agarose gels after RedSafeTM staining under UV light and, for poor quality DNA samples, re-extraction was performed by DNeasy Plant Mini Kit  on 0.1 g young leaves frozen in liquid nitrogen. Thirteen SSR markers were analyzed, i.e., the nine included in the GrapeGen06 set (see introduction) and four others  that are routinely used at the “Consiglio per la Ricerca in Agricoltura e l’analisi dell’Economia Agraria-Uva da Tavola” . Four m ITA388, ), the Itl et al.  in orderThe main variety name, the use and the country of origin to each identified genotype was assigned according to the information given on the databases consulted (see above), mainly VIVC. Non-matching genotypes as well as missing information were annotated as “unidentified”. The genetic grouping of the germplasm under study—considering just the unique genetic profiles detected—was explored through different statistical methods.A Bayesian clustering algorithm to sort individuals into K clusters (subpopulations) according to their genetic similarity was performed using STRUCTURE 2.3.4 . The beshttps://www.r-project.org (accessed on 12 April 2022)). Initially, the SSR dataset was examined by using the discriminant analysis of principal components (DAPC) implemented in the Adegenet package ver. 2.0.1 [Data were also evaluated using the Poppr package  in R  tree was constructed based on the Simple Matching dissimilarity index (SM) between the unique genetic profiles using Darwin software package v6.0 . One thoVitis vinifera genotypes. Following statistics were determined with GenAlEx 6.0 [Vitis vinifera population; (b) for the main country groups according to VIVC information; (c) for each cluster pointed out by STRUCTURE. We also studied the hierarchical genetic variation among and within inferred subpopulations by AMOVA and their genetic differentiation by the F-statistic , including the calculation of FST for each group pairwise comparison.Genetic diversity analyses were conducted on the pool of AlEx 6.0 : number AlEx 6.0  was usedvinifera set were constructed using the M (maximization) method, suggested by Schoen and Brown [Two genetic core collections of the nd Brown  and implnd Brown . In the Vitis vinifera pool and the clusters inferred by structure analysis. Clustering of samples at k = 2 discriminates grapes cultivated in a Mediterranean climate from Continental-Oceanic ones, while k = 7 infers more restricted ecogeographic areas of cultivation. A core collection capturing allele richness may be suitable for association studies given that an equilibrated accession contribution from each of the six main eco-geographic inferred groups was achieved. Varieties from the Iberian Peninsula cultivated under Mediterranean conditions form a quite genetically homogenous group and, in accordance with previous studies, our results confirm that the origin of many Spanish  varieties is strongly related to both local Vitis sylvestris and Eastern Vitis vinifera cultivars introduced in Spain by trade.The genetic characterization of this grapevine collection conserved at Rancho de la Merced has allowed to identify mislabeling and redundant accessions, somatic and molecular variants, as well as new grapevine genetic resources. These results will help to optimize the management of one of the most important international grapevine germplasm repositories. In addition, the information obtained could represent an important updating of the VIVC. A consistent genetic diversity has been revealed in both the full


The quantitative study of the relationship between material composition, microstructure and properties is of great importance for the improvement in material properties. In this study, the continuous data of elemental composition, recrystallization, hardness and undissolved phase distribution of the same sample in the range of 60 to 150 square millimeters were obtained by high-throughput testing instrument. The distribution characteristics and rules of a single data set were analyzed. In addition, each data set was divided into micro-areas according to the corresponding relationship of location, and the mapping between multi-source heterogeneous micro-area data sets was established to analyze and quantify the correlation between material composition, structure and hardness. The conclusions are as follows: (1) the average size of the insoluble phase in the middle of the two materials is larger than that of the surface, but due to the existence of central segregation, the average area of the T4 insoluble phase showed an abnormal decrease; (2) there was positive micro-segregation of Al, Cr, Ti, and Zr elements, and negative micro-segregation of Zn, Cu, and Fe elements in the recrystallized grains of the T5 middle segregation zone; (3) the growth process of the insoluble phase was synchronous with the recrystallization proportion and the size of the recrystallized grains; (4) the composition segregation and recrystallized coarse grains were the main reasons for the formation of low hardness zone in T4 and T5 materials, respectively. It has been widely used in high-speed train components such as traction beams, corbel beams and buffer beams ,2,3. TheThe mechanical properties of the rolled sheet along the thickness direction are different due to the differences in the surface and center structure ,7,8,9,10The composition difference between the surface and the central part is also an important reason for the difference of mechanical properties. The results show that 7B05 aluminum alloy has both microscopic segregation in several grain ranges and macroscopic segregation at the centimeter level . MacroscAt present, the problems of low stability and poor durability seriously restrict the localization process of aluminum alloy materials for high-speed rail. The fundamental reason for this is that the internal micro-scale composition and structure of the material have low control accuracy and experience large fluctuations. Therefore, it is of great significance to study the uniformity of aluminum alloys in terms of the microscopic composition and structure. However, the existing characterization methods of composition and structure cannot achieve continuous data acquisition, and can only obtain local information to replace the overall state of the material, ignoring the influence of the inhomogeneity of the material itself. In this study, the concepts of material genetic engineering and big data ,19,20,21The commonly used quantitative methods of organizational structure are usually implemented by image processing software along with manual participation. This process requires significant time and labor inputs; thus, the statistical results are acquired only from limited features and regions ,23,24. IThe common analysis methods for component distribution, such as scanning electron microscopy, energy spectrum analysis and electron probe microanalysis, are only aimed at micro-area test, with slow analysis speed and low quantitative sensitivity ,29,30. SOriginal statistical spatial-mapping technology is characterized by the originality, in-situ properties and statistics of information, which can reflect large-scale statistical distribution trends of the composition and microstructure of metal materials ,37,38,39The 7B05 aluminum alloy of the same brand and four specifications for commercial sleeper beams was used in the experiment. This high-speed rail material was provided by China Railway Rolling Stock Corporation . After hot rolling, the material was heat treated with natural aging (T4) and artificial aging (T5). Its chemical composition is shown in The polished sample was placed in the Navigator OPA high throughput scanning  electron microscope to collect images. The acquisition field sizes were as follows: T4-6 aluminum alloy, 10 mm × 6 mm; T4-15 aluminum alloy, 10 mm × 15 mm; T5-10 aluminum alloy, 10 mm × 10 mm; T5-15 aluminum alloy, 10 mm × 15 mm. The number of images collected from each of the four samples was 3362 (T4-6), 11,508 (T4-15), 7056 (T5-10) and 10,668 (T5-15), and the pixel size of each image was 4096 × 4096. The magnification of T4-15, T5-10 and T5-15 images was 2800 times, and of the T4-6 image was 2000 times.The quantitative statistical characterization method of the second phase was established based on the deep learning algorithm. The experimental process is shown in The network we constructed in the dotted box based on the original U-Net architecture consisted of a contracting path and an expanding path ,44,45. TFirstly, samples were polished with 400#, 800#, 1200#, 2500# and 4000# sandpaper, and then polished with 0.5 µm silicon dioxide and a fine polishing cloth until the surface scratches were completely removed. Secondly, samples were eroded with the erosion solution “Keller reagent”, and the erosion time was about 40 s. Microstructure images were acquired using the Leica DM6000 metallurgical  microscope with autofocus and continuous acquisition mode. The field sizes were as follows: T4-6 aluminum alloy, 10 mm × 6 mm; T4-15 aluminum alloy, 10 mm × 15 mm; T5-10 aluminum alloy, 10 mm × 10 mm; T5-15 aluminum alloy, 10 mm × 15 mm. The magnification was 50 times, the number of images collected from the four samples was 24 (T4-6), 50 (T4-15), 35 (T5-10) and 50 (T5-15), and the pixel size of each image was 2400 × 1800. Then, the same parameters were set to stitch images so that a full-field image could be obtained for one sample. Finally, the stitched image was processed to increase the contrast. According to the threshold of micro-area division, the image matrix was divided by MATLAB software , and the average gray value of each micro-area was obtained.The element composition distribution of the aluminum alloy section was analyzed by microbeam X-ray fluorescence spectrometer . The detailed parameters were as follows: X-ray tube voltage was 50 kV, current was 150 μA, target material was Rh, beam spot size was 20 μm, beam spot collection interval was 10 μm, scanning time per pixel was 100 ms, and sample chamber vacuum was 20.1 mbar.According to the ISO 6507-1-2005 standard, a Vickers hardness tester  was used to measure the hardness of the aluminum alloy section. The multi-point measurement mode was adopted to ensure that the distance between the centers of two hardness points was greater than 3 times the diagonal distance. The test load was 10 N and the loading time was 10 s. The hardness matrix data with equal intervals on the sample surface was processed by interpolation, and the interpolation matrix could be considered to represent the actual hardness distribution of the sample cross section. Taking T5-10 as an example, the measurement area was 10 mm × 10 mm, and the number of hardness points was 4150.3PO4 + 25% C2H5OH + 50% H2O mixed solution. The electrolysis voltage was 15 V, and the electrolysis time was 30 s. EBSD was observed by a TESCAN S8000G  scanning electron microscope; the scanning working distance was 14 mm, the acceleration voltage was 20 KV, the current was 3 nA, the step size was 0.5 μm, and the scanning area size was 500 pixels.After cutting, the sample was polished with 400#, 800#, 1200#, 2500# and 4000# sandpaper, and then polished with aluminum oxide and a velvet cloth until the surface scratches were completely removed. In order to further eliminate the residual stress on the surface of the sample, electrolytic polishing was carried out. The polishing liquid was 25% HThe surface layer structure is composed of equiaxed crystals and abnormal grains grown after recrystallization, as shown in It can be seen from IPF that the maximum orientation densities of the surface and the center of the two materials T4-6 and T5-10 are quite different. Because of the thickness of the sheet, T4-6 and T5-10 are more significantly affected by hot rolling deformation. As is conveyed by The dislocation angle distribution and grain size of the surface and center layers of the 7B05 material in the range of 500 μm × 500 μm are shown in 7Cu2Fe. The black inclusion phases have high content of Mg and Si and may exist in the form of compound Mg2Si.2) distribution of the second phase on the sections of four aluminum alloy rolled plates was counted. The U-Net target model based on deep learning was used to segment the image to be measured. After segmentation, the full field  where the gray value drops sharply based on It is difficult to use a point-to-point method to explore the correlation between the two sets, since the component and grayscale data are two sets of data having different scales and different types. This study used the micro-region–micro-region method to explore the data rules and determine the degree of correlation between the two sets. The T5-10 section (10 mm × 10 mm) was divided into a 14 × 14 micro region, and the element content and grayscale data of 186 areas at the corresponding positions were extracted. The relationship between grayscale and element content is shown in It can be seen from With the increase in gray value, the contents of Cr, Ti and Zr in the front part decrease first and then increase. The locations with the lowest gray values are distributed near the surface, and the degree of solid solution is the highest in this area. In the process of centripetal extension, the decrease in the content caused by the weakening of the solid solution is greater than the increase in the content caused by the proportion of recrystallization. Therefore, the front part of the fitting lines of Cr, Ti and Zr elements shows a decreasing trend. However, there is no change in the solid solubility of the Al element, and the distribution shows a slowly increasing trend. The statistical mapping trend of T4 is similar to that of T5, as shown in The section was divided into micro-areas for mapping, the gray value and hardness data of the corresponding micro-areas were extracted, and the abnormal data having a value of less than one tenth was filtered out. The relationship between gray value and hardness is shown in In summary, the hardness distribution is related to the structure. The surface equiaxed crystal has a small hardness value and uniform distribution; with the occurrence of T5 material recrystallization, the hardness in the transition zone rises to the highest value; when the recrystallization ratio is the highest, the hardness is the lowest. The recrystallized grain size on the intermediate layer of T4 material is smaller than that of T5 , and theThe original statistical mapping technique used in this study is based on the identification of intrinsic heterogeneity to characterize aluminum alloy samples with high-throughput and cross-scale statistical distribution, resolving the correlation building problem among element distribution, properties and microstructure over a large area. The region–region original statistical mapping method was selected, and the original data set was divided by selecting an appropriate region as the minimum unit. The characterization parameters of the micro-area in the muti-source data set were extracted; this process is a nonlinear down-sampling function, which reduces the space size in exchange for the increase in the dimension of parallel representation data. Through the mining of multi-source heterogeneous data sets, the correlation model between each data set was finally established, as shown in (1)2 in the surface layer, to 3 μm2 in the center layer. The anomalous decrease in the insoluble phase area near the center is related to the center segregation. The growth in the insoluble phase of T5 materials is synchronized with the growth process of recrystallization.The number of insoluble phases in T4 and T5 has the same variation trend, with more in the surface layer and fewer in the middle. The average area of the insoluble phase in two T4 materials increases from 1.5 and 1 μm(2)The surface of the rolled sheet has a high degree of solid solution, and the composition is evenly distributed; for T5 material, there is positive segregation of Al, Cr, Ti and Zr, and negative segregation of Zn, Cu and Fe in the recrystallized grains of the middle segregation zone; T4 material has genetic center segregation caused by casting and hot rolling process.(3)The hardness value of the surface composed of equiaxed and coarse recrystallized grains is small and the distribution is uniform; the band-like hardness distribution in the T5 intermediate layer is related to the recrystallization proportion and the size of the recrystallized grains. The coarse grain in the T4 intermediate layer is smaller than that of the T5 material; therefore, there is no low-hardness zone with a band-like distribution. The low hardness region with a value below 125 in T4 is mainly caused by the negative segregation of alloying elements. Values below 100 are basically distributed in this area.(4)The original statistical spatial-mapping method demonstrated the feasibility of analyzing the correlation among massive multi-source heterogeneous data, including for composition–microstructure–performance.


Petaurus breviceps) sampling locations within a fragmented landscape prior to the implementation of a wildlife corridor. This study used 5999 genome-wide SNPs from 94 sugar gliders caught from 8 locations in a fragmented landscape in south-eastern New South Wales, Australia. Overall genetic structure was limited, and gene flow was detected across the landscape. Our findings indicate that the study area contains one large population. A major highway dissecting the landscape did not act as a significant barrier to dispersal, though this may be because of its relatively new presence in the landscape (completed in 2018). Future studies may yet indicate its long-term impact as a barrier to gene flow. Future work should aim to repeat the methods of this study to examine the medium-to-long-term impacts of the wildlife corridor on sugar gliders, as well as examine the genetic structure of other native, specialist species in the landscape.Habitat loss and fragmentation contribute significantly to the decline of arboreal mammal populations. As populations become fragmented and isolated, a reduction in gene flow can result in a loss of genetic diversity and have an overall impact upon long-term persistence. Creating wildlife corridors can mitigate such effects by increasing the movement and dispersal of animals, thus acting to reduce population isolation. To evaluate the success of a corridor, a before–after experimental research framework can be used. Here, we report the genetic diversity and structure of sugar glider ( Habitat loss and fragmentation are the greatest threatening processes for mammalian species in the Anthropocene . GloballThe effects of habitat fragmentation are particularly pronounced for arboreal species; those that are strictly bound to the canopy. Mammals that are obligate arboreal, with limited dispersal capability in a matrix that is cleared, are some of the most susceptible to extinction . The disFragmentation can affect the population genetic diversity and structure within arboreal mammal populations due to the isolation of patches and the inability for individuals to disperse ,14,15,16Wildlife corridor implementation is considered an important strategy to reconnect habitat patches in fragmented landscapes ,22,23. ITo evaluate the implementation of these corridor conservation strategies, a before–after control–impact (BACI) design can be used ,35. A BAPetaurus breviceps) to assess the impact of habitat fragmentation and other landscape-scale movement barriers  prior to the establishment of a wildlife corridor in south-eastern Australia. Sugar gliders are arboreal and rely on trees for movement; however, they can glide up to 30 m to move between trees and across gaps in canopy [Here, we use a BA design on the arboreal sugar glider , located approximately 100 km south of Sydney, Australia . The areLive trapping occurred across 25 sites between 2018 and 2020, and 94 sugar glider individuals were caught at 16 of these sites. Based on proximity and the surrounding matrix surrounding these sites, these samples were pooled into eight groups for analyses . The eigGymnobelideus leadbeateri, GCA_011680675.1 LBP_v1′) using DArTseq analytical pipelines. Quality control filtering ensured high average read depth per locus . For additional information regarding the DArTseq process and associated quality control procedures, refer to [A total of 94 sugar glider tissue samples were sent to Diversity Arrays Technology Pty Ltd. (DArT)  for DNA extraction followed by next-generation sequencing. Through next-generation sequencing and genome-complexity-reduction methods, DArTseq is able to use bi-allelic, codominant SNP markers . The resrefer to .p < 0.05) [Once the DArTseq filtering was complete, 24,682 genome-wide SNPs were retained from high-quality samples with a minimum sequence identity of 70%. Further filtering was conducted in R 4.0.2  using th < 0.05) . No monoo) and expected heterozygosity were calculated at each SNP (He) using the package ‘hierfstat’ [IS) for all gliders across the entire landscape, as well as for each putative population using all 5999 genome-wide SNPs.All analyses were undertaken in R 4.0.2 . For locerfstat’ . These vST values were calculated between the eight sampling locations using the ‘dartR’ R package [p-values were adjusted with a Bonferroni correction (see Results for details) [K) contributing to differentiation between sampling locations [K value selected was based on the lowest value of Bayesian information criterion (BIC) values, and where the following K values changed by negligible amounts [K = 1), further STRUCTURE analyses were not warranted, as bias within the analyses would lead to detection of artificial genetic clusters [To examine population genetic structure, pairwise F package  with 10,details) . An analdetails) . A princdetails) . Lastly,details) , discrimocations . The K v amounts . As all clusters .We tested if fragmentation of habitat would lead to a decrease in landscape functional connectivity by impeding gene flow among sampling locations. This was performed by comparing two alternative models to predict patterns of genetic structure. Firstly, a null model, the isolation-by-distance (IBD) model, assumes that gene flow is only constrained by geographic distance and that genetic differentiation between sampling locations is expected to increase with Euclidian distance . The secIS values were greater than zero, indicating some amount of inbreeding, whereas for the Woodhill location there was a negative value, indicating an avoidance of inbreeding  was explained by within-individual sugar glider variations, while only 5.12% of the genetic variation could be attributed to individuals within sampling locations . The remST values that were found to be significant included the adjacent SMBNP and Coomonderry (FST = 0.011), which had low genetic differentiation, whereas higher genetic differentiation (FST = 0.090) was found between Woodhill (located west of the highway) and Coomonderry, located east of the highway, 7.51 km away (direct line of distance).Pairwise genetic population structure estimates were all significant except for the comparison between the locations of Boundary Road and Tindalls Lane . PairwisThe DAPC analysis found BIC values to increase with the number of clusters tested . There wp = 0.0004) (p = 0.0754). The LCP analysis identified several wildlife corridor pathways of connected canopy and routes that utilised wildlife crossings over the highway  . A least highway .The gene flow of arboreal marsupials can be negatively affected by fragmentation. This is particularly relevant where intense agriculture ,40,69, uST analyses all indicated one large population. A strong IBD effect was found for our null model, where geographic distance between sampling locations correlated strongly with genetic distances. This indicates that the landscape does not contain significant barriers to movement.No population genetic structure was observed between the sampling locations within the study landscape. Results from the PCoA, AMOVA, DAPC, and pairwise FFor the sugar glider, one study found that genetic structure and diversity  was impacted by habitat fragmentation . Their sPetaurus norflocensis), evidence of a genetic structure has been found, which was attributed to the presence of barriers and habitat fragmentation [Pseudocheirus peregrinus) [Pseudocheirus occidentalis) [Muscardinus avellanarius) [Alouatta caraya) [Despite the limited genetic structure found in our study, there is evidence of genetic structure in fragmented populations in many other species similar to the sugar glider. For several populations of the squirrel glider  , westernentalis) , edible entalis) , hazel danarius) , and bla caraya) . These a caraya) ,76,77,78 caraya) . Thus, t caraya) ) or crosAntechinus stuartii) [Petauroides volans) is threatened by isolation at SMBNP. The species is absent from the fragmented landscape between SMBNP  and the continuous habitat of the Illawarra Escarpment  . The stutuartii) . It was tuartii) . The higSugar gliders can still be used to evaluate the effectiveness of the wildlife corridor in the long term ,34. ChanThe impacts of the wildlife corridor are likely to take effect between now and the next 100 years, as planted trees reach maturity and begin to form the critical hollows needed by arboreal mammals for denning and raising young . We prov


Using the 2-methylene)malononitrile fluorophore  and the recognition group β-Glucose, an enzymatic activatable turn-on fluorescent probe (DXM-Glc) was developed for the selective and sensitive sensing of β-Glc. In addition, DXM-Glc could be used to sense endogenous β-Glc in living fungal cells. Using DXM-Glc, Pichia terricola M2 was identified as a functional intestinal fungus with β-Glc expression. P. terricola M2 could transform the flavone glycoside Icariin to Icariside Ⅱ efficiently, which confirmed the metabolism of glycosides in the gut mediated by fungi. Furthermore, Icariside Ⅱ could inhibit the proliferation of human endometrial cancer cells (RL 95-2 and ishikawa) significantly, suggesting the metabolic activation of Icariin by intestinal fungi in vivo. Therefore, DXM-Glc as a probe for β-Glc provided a novel technique for the investigation of the metabolism of bioactive substances by intestinal microbiota. As a biocatalyst, β-Glc was been widely used in industrial preparations, exhibiting the advantages of improved conversion rate, mild reaction conditions, while being environmental-friendly, and enabling the simple purification of products express β-Glc, and participate in the metabolism of glycosides and carbohydrates from foods and pharmaceuticals has been developed determining β-Glc activity sensitively and selectively. When combined with cultures of intestinal fungi, visual identification of target fungus expressing β-Glc was successfully achieved. Furthermore, using the identified intestinal fungus, Icarisid Ⅱ was released as a metabolite of Icariin, which exhibited significant cytotoxicity toward endometrial cancer cells.In this study, a turn-on fluorescent probe . β-Glucosidase (β-Glc), α-Glucosidase (α-Glc), Lipase, Human serum albumin (HSA), β-Galactosidase (GAL), and β-N-Acetylglucosaminidase (NAG) were obtained from sigma-aldrich . Recombinant human carboxylesterases  were purchased from Corning Incorporated Life Sciences.Chromatographic methanol for HPLC was purchased from sigma-aldrich . All of the chemical reagents and solvents for the synthesis were obtained from Tianjin Kemiou Chemical Reagent Co., Ltd . The agar, glucose, NaNMR spectra were measured using Bruker-600 with tetramethylsilane (TMS) as the internal standard . HRESIMS data were acquired on an AB SciexX500r TOF mass spectrometer . Fluorescence microscopic imaging was conducted using a Leica Confocal Microscope . The bioassay solutions in 96-well plates were analyzed using a BioTek Synergy H1 microplate reader . HPLC-UV analysis was performed using a Waters e2695 .DXM-Glc was synthesized according to the route shown in DXM-OH  in 10 ml of dry DCM, 2,3,4,6-tetra-O-acetyl-α-d-glucosyl bromide , Cs2CO3  were added. The reaction mixture was stirred at room temperature overnight, filtered, and evaporated. The residue was dissolved in 10 ml of MeOH, and CH3ONa  was added. The mixture was stirred at room temperature for 1 h, neutralized with 1M HCl, filtered, and evaporated. The residue was purified by HPLC to afford 20.5 mg DXM-Glc as dark red powder. Yield: 23.4% in two steps. The chemical structure of DXM-Glc was determined by 1H, 13C NMR and HR-MS data  δH 8.17 , 7.46 , 7.36 , 7.25 , 6.96 , 5.41 , 5.15 , 5.08 , 5.04 , 4.59 , 3.69 , 3.49 , 3.25 , 3.19 , 3.12 , 2.75 , 2.63 , 1.82–1.69 . 13C NMR  δC 160.22, 159.51, 153.74, 150.53, 132.39, 128.88, 126.82, 118.07, 116.63, 116.11, 114.61, 109.94, 103.66, 100.39, 77.55, 76.96, 73.61, 69.98, 68.11, 61.04, 28.57, 24.80, 20.44. HRMS cacld for [M + H]+ 439.1500, found m/z 439.1508.β-Glc (100 μg/ml) and DXM-Glc  were co-incubated at 37°C for 30 min. Then, dimethyl sulfoxide  was added to inactivate the β-Glc activity and terminate the enzymatic reaction. When the denatured proteins were precipitated by centrifugation at 20,000 ×g for 20 min, the fluorescence intensity corresponding to the production of DXM-OH was measured using a Microplate reader with an excitation wavelength of 600 nm and emission wavelength of 636 nm.In a phosphate buffer solution , DXM-Glc (10 μM) toward β-Glc with different concentrations  has been recorded using an excitation laser at 600 nm. The linear relationship between the fluorescence intensity and β-Glc concentrations was calculated using linear regression equation. The limit of detection (LOD) was calculated using 3σ/slope.The fluorescence response of +, Na+, Mg2+, Ca2+, Ba2+, Zn2+, Cu2+, Ni2+, Sn2+, Mn2+, CO32-, SO42- and amino acids (200 μM) were co-incubated with DXM-Glc in phosphate buffer at 37°C for 30 min, respectively. Then, the fluorescence intensity was measured using a Microplate reader (λex 600 nm/λem 636 nm).Various ions including KDXM-Glc toward β-Glc was evaluated in the presence of other biological enzymes β-Glc, α-Glc, HSA, GAL, NAG, CES1b, CES1c, and CES2 (100 μg/ml). The fluorescence responses of DXM-Glc toward these enzymes were measured using Microplate reader (λex 600 nm/λem 636 nm).The selectivity of 2PO4 1 g/L, Mg2SO4 0.5 g/L) at 32°C, 160 r/min. 36 h later, the probe DXM-Glc was added with a final concentration of 20 μM and incubated for 4 h. Then, the fluorescence intensity was measured using Microplate reader (λex 600 nm/λem 636 nm). Similarly, after incubation, Pichia terricola M2 cells were washed with PBS and diluted in saline. The suspensions were dropped on glass slides and imaged using a laser confocal microscope (λex 561/λem 600–660 nm).The fungi strains were cultured in martin broth modified . Then, the supernatant was inactivated by acetonitrile for HPLC analysis, which was used to confirm the production of Icariside II. Icariside II was purified by extraction of the fermentation broth using ethyl acetate, and the metabolite was then purified using preparative HPLC.1H-NMR  δH 12.57 , 7.89 , 7.13 , 6.63 , 5.35 , 5.28 , 5.16 , 5.12 , 5.05 , 5.00 , 4.98 , 4.73 , 4.66 , 4.62 , 4.00 , 3.85 , 3.71 , 3.57 , 3.46 , 3.30 , 3.14 , 3.08 , 1.69 , 1.60 , 0.79 . 13C-NMR  δC 178.30, 161.42, 160.52, 159.09, 157.33, 153.02, 134.65, 131.12, 130.57, 122.27, 122.14, 114.09, 108.30, 105.60, 101.99, 100.54, 98.13, 77.19, 76.61, 73.36, 71.11, 70.71, 70.31, 70.08, 69.66, 60.63, 55.51, 25.47, 21.42, 17.87, 17.46. HR-MS: m/z 677.2430, [M + H]+, calcd for C33H41O15, 677.2440  δH 12.52 , 10.85 , 7.85 , 7.11 , 6.31 , 5.26 , 5.15 , 4.97 , 4.71 , 4.64 , 3.98 , 3.85 , 3.47 , 3.41 , 3.33 , 3.13 , 3.06 , 1.67 , 1.02 , 0.78 . 13C-NMR  δC 177.99, 161.68, 161.28, 158.85, 156.75, 153.79, 134.43, 131.03, 130.42, 122.41, 122.27, 114.06, 105.95, 104.17, 101.96, 98.35, 71.11, 70.65, 70.30, 70.07, 55.49, 25.43, 21.17, 17.79, 17.46. HR-MS: m/z 515.1909, [M + H]+, calcd for C27H31O10, 515.1912  were inoculated into a 16-well culture plate of the cell analyzer. Then, a certain concentration of Icariin II was added respectively . After mixing, the sample was placed in the real-time unmarked cell analyzer, and the cell proliferation curve was obtained after 48 h.First, serum-free medium was added to the real-time label free cell analyzer to remove the background value, and cells . After incubation for 24, 48, and 72 h, CCK-8 was added, and the absorbance was measured at 450 nm by EnSpire® Multimode Plate Reade . Cell viability in the vehicle control groups was considered 100%. Each assay was carried out in triplicate.Cell viability was determined using a CCK-8 assay. In brief, 3 × 103 per well) were seeded in 6-well plate containing 2 ml growth medium with 10% FBS and cultured for 24 h. Then, the medium was removed, and cells were treated with various concentrations of Icariside II . After 24 h, cells were washed with PBS and supplemented with fresh growth medium, cells were routinely incubated for about 10 days until colonies were large enough to be visualized. Then colonies were stained with 0.1% crystal violet and counted.To analyze the effects of Icariside II on colony formation, single cells . Analysis of variance and Student’s t-test were used to compare the values of the test and control samples. β-Glc is known to mediate the cleavage of β-O-glycosidic bond of glycosides and glycans. The stereo configuration of the glycoside was specifically recognized by β-Glc. According to the catalytic characteristics of β-Glc, a fluorescent substrate was developed with a glucose group grafted through a β-O-glycosidic bond. 2-methylene)malononitrile (DXM-OH) was chosen as an intramolecular charge transfer (ICT) fluorophore which exhibited good photostability and excellent biocompatibility  was developed for sensing of β-Glc , while DXM-OH exhibited strong fluorescence, which could be used detect β-Glc sensitively and without interference from biological samples  was calculated to be 0.02 mU/mL (0.2 μg/ml) using 3σ/slope, indicating excellent sensitivity of DXM-Glc toward β-Glc. As a designed fluorescent probe for β-Glc, the substrate specificity of DXM-Glc was evaluated in presence of various species. Common ions and amino acids exhibited no interference for the fluorescence intensity of DXM-Glc, indicating the suitability of the system for application for the assay of biological samples , a natural flavonoid glycoside containing glucose and rhamnose, is the major constituent of herba Epimedii (>0.05%). ICA exhibits various biological activities, including anti-inflammatory, antidepressant, antioxidative, antiatherosclerosis, anticancer, and insulin resistance. Icarisias ICA II. Though Pichia terricola M2 could catalyze the cleavage of the β-O-glycoside bond between glucose and flavone, producing ICA II as the metabolite   , which iPichia terricola M2. It was proposed that ICA Ⅱ would exhibit improved membrane permeability enhancing bioavailability. As such, we evaluated the cytotoxicity of ICA Ⅱ toward two endometrial cancer cell lines. Therefore, we monitored the growth curves of RL95-2 and ishikawa for >60 h, to determine the effect of ICA Ⅱ on cancer cells. From these experiments it was clear that ICA Ⅱ could inhibit the growth of RL95-2 at 20 μM, and interfere with the growth of ishikawa at 10 μM  for β-Glc. DXM-Glc exhibited high selectivity and sensitivity toward β-Glc. Using DXM-Glc, Pichia terricola M2 was identified as intestinal fungus expressing β-Glc, which could transform ICA to ICA Ⅱ efficiently. Furthermore, ICA Ⅱ was found to significantly inhibit the proliferation of human endometrial cancer cells. Therefore, the DXM-Glc probe can be used to evaluate the β-Glc activity of intestinal microbiota and monitor the metabolism of bioactive substances.


Female fertility is a heterogeneous condition of complete psychosocial and physical well‐being. Observational studies have revealed that women with infertility have varying degrees of poor mental status and lifestyle choices in varying degrees. However, the genetic contribution to female infertility remains elusive. Our study aimed to explore the genetic correlations between female infertility and mental health and lifestyle factors.The genome‐wide association study (GWAS) data sets of characteristics related to mental health and lifestyle were obtained from the IEU OpenGWAS database. The GWAS data sets of female infertility were derived from the Finggen database. Linkage disequilibrium score regression was performed to systematically estimate the pairwise genetic correlations between female infertility and a set of mental health‐ and lifestyle‐related traits.The genetic correlation analysis revealed a moderate and positive genetic correlation between depressive symptoms, major depressive disorder, and female infertility. Similarly, worry and the personality trait of neuroticism displayed a moderate positive genetic correlation with female infertility. Adversely, a negative and moderate genetic correlation was observed between strenuous sports or exercises and female infertility.The study demonstrated genetic correlations between female infertility and mental health status, including depression, worry, and neuroticism. Additionally, we observed that females with better physical activity may have reduced risks of female infertility. These findings would serve as a fundamental resource for understanding the genetic mechanisms of the effects of mental health and lifestyle factors on female infertility. We used cross‐trait LDSC to systematically estimate the pairwise genetic correlation of female infertility with mental health and lifestyle factors.22.12.1.1β‐coefficients and corresponding standard errors) from FinnGen on female infertility comprised 6481 cases and 68,969 controls (https://r5.finngen.fi/pheno/N14_FEMALEINFERT). The inclusion criterion was females diagnosed with infertility. The diagnosis of female infertility in FinnGen was defined by N97 in the International Classification of Diseases (ICD) 10 code. The exclusion criterion was male infertility . Females who were not diagnosed with infertility were considered controls. Methods  are demonstrated on the FinnGen webpage.The FinnGen study, an ongoing nationwide cohort study launched in 2017, combines genetic data from the Finnish Biobanks with health record data from the Finnish health registries. In FinnGen, GWAS summary statistics for >1800 phenotypes/endpoints have been publicly released. Finland is a best‐studied genetic isolate and a unique gene pool of the Finns. GWAS summary‐level data (2.1.2https://gwas.mrcieu.ac.uk). This website provides open access to over 10,000 complete GWAS summary data sets.The GWAS summary statistics of mental health‐ and lifestyle‐related traits were selected from the IEU OpenGWAS database  between common traits or diseases based on GWAS summary statistics. Bivariate LDSC analyses were performed using the LDSC software version 1.0.1 (https://github.com/bulik/ldsc).χ2 statistics on their LD scores.p < 0.05 was considered significant. All statistical tests were two‐sided. The analyses were performed using R version 3.6.3 statistical software .LDSC analysis is a powerful tool to investigate the shared genetic components (33.1rg) and standard errors between female infertility and traits of mental health status are presented in Figure rg = 0.45, p = 0.016, FDR‐adjusted p = 0.032). The genetic correlation result of depressive symptoms was similar to that of major depressive disorder. The genetic correlation was moderate and significant . The personality trait of neuroticism revealed moderate positive genetic correlation with female infertility . Additionally, moderate genetic correlation was observed between female infertility and worry .The genetic correlations .The genetic correlations between female infertility and other traits of mental health conditions, including anorexia nervosa, bipolar disorder, schizophrenia, and autism spectrum disorder, 3.2rg  =  −0.42, p  =  0.001, FDR‐adjusted p = 0.007). We observed no genetic correlation between female infertility and sleep duration  and smoking . Similarly, we observed no genetic correlation between alcohol, vitamin D levels, and female infertility .The genetic correlations between female infertility and lifestyle factors are presented in Figure 4To our knowledge, our study is the first to investigate genetic correlations between female infertility and multiple mental health‐related traits and lifestyle factors. We demonstrated significant genetic correlations between female infertility and poor mental health and strenuous sportsPrevious studies have demonstrated that women with infertility reported elevated levels of anxiety and depression.We observed a significant genetic correlation between neuroticism and female infertility, which has been reported in previous observational studies.We observed a modest and significant genetic correlation between strenuous exercise and female infertility using the LDSC approach. The negative correlation reflected a difference in genetic background between female infertility and strenuous exercises. Physically active females were more likely to be fertile, consistent with a previous observational study reporting that more females with normal fertility engaged in moderate and vigorous activities.Smoking phenotypes and alcohol use are genetically correlated with many health conditions.There were several strengths to our study. First, our study addressed the genetic correlation between female infertility and other diseases or traits. Second, we believe that LDSC is the most effective tool for genetic correlation analysis since the summary statistics are available for much larger sample sizes than those with individual genotype data. Third, in contrast to observational studies, our LDSC analysis supported a reliable methodology to assess the association between complex traits while minimizing the possibility of bias owing to unknown confounding. This study had some limitations. We did not estimate sex‐based genetic correlation. Female infertility phenotypes only included females, whereas samples of mental health disorders and lifestyle‐related factors included both sexes. Furthermore, most phenotypes used the UK Biobank data, which limits the generalizability of results to other ancestries. For a larger sample size, large‐scale genetic studies that replicate findings across other ancestry groups will be useful. Finally, the clinical significance of some poor or modest genetic correlations remained elusive. Future work with an increased sample size to replicate these findings is warranted.5Conclusively, by utilizing the LDSC approach, we evaluated the association between mental health status, lifestyle factors, and female infertility. Our study identified significant and positive genetic correlations between multiple mental health conditions and female infertility. We also revealed that strenuous exercise is negatively correlated with female infertility. We believe that our findings would serve as a fundamental resource for understanding the genetic mechanisms of the effects of depression, anxiety, neuroticism, and lifestyle factors on female infertility. Furthermore, we believe that our study lends support to future research into the mental health and lifestyle of females with infertility.Conceptualization: Miao Ma, Lu Guo, and Bin Li. Formal analysis: Miao Ma, Xiaocheng Liu, and Yingxin Zheng. Writing—review and editing: Chao Gu and Bin Li. Writing—original draft: Miao Ma. All authors have read and approved the final version of the manuscript. Miao Ma had complete access to all the data in this study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis.The authors declare no conflict of interest.Miao Ma affirms that this manuscript is an honest, accurate, and transparent account of the study that has been reported, that no important aspects of the study have been omitted, and that any discrepancies from the study as planned  have been explained.


The genetic diversity of 21 chayote accessions was also examined using six microsatellite markers. A global low genetic heterozygosity (Ho = 0.286 and He = 0.408) and three genetic groups were detected. The results established the basis to provide insights into chayote arrival history in Asia by looking at the crop’s reproductive morphology, cytology, and genetic diversity status outside its origin center. This could help in developing sustainable utilization and conservation programs for chayote.Promoting neglected and underutilized crop species is a possible solution to deal with the complex challenges of global food security. Chayote is a Neglected and Underutilized Cucurbit Species (NUCuS), which is recognized as a fruit vegetable in Latin America and is widely grown in Asia and Africa. However, basic biological knowledge about the crop is insufficient in scientific sources, especially outside of its center of origin. In this study, limited observations on reproductive characters were conducted, differentiating accessions from Mexico, Japan, and Myanmar. Cytological evaluation among Mexican and Japanese accessions showed that the relative nuclear DNA content is 1.55 ± 0.05 pg, the estimated genome size is 1511 at 2C/Mbp, and the observed mitotic chromosomal number is 2 The biodiversity crisis began many years ago since human activities started to severely impact the global environment. The high rates of species extinction sped up the process of eroding the environment that human society depends on . In the One possible solution to deal with the complex challenges of global food security is to promote neglected and underutilized crop species. Given the vast repository of these crops, correct identification of the species with the potential to ease the stress of the food crisis around the world is another challenge for modern agriculture. In addition, the potential crops also need to withstand harsh environmental stresses and should have highly valuable market characteristics .Sechium edule (Jacq.) Sw. is considered a Neglected and Underutilized Cucurbit Species (NUCuS) .Pollen was also collected from the anthers following the method used by Qureshi et al. (2009) . Mature virens levis (KW019) was used for this part. Anthers were taken at 9–10 AM from October to December 2019, then crushed and kept in airtight small plastic container with high humidity in dark for incubation. Basic germination medium follows the protocol as Vižintin and Bohanec (2004) described [w/v), 0.25 mg/L H3BO3, and 1 mM Ca (NO3)2 at pH 7.0. After 60 min incubation, 500–600 pollen grains per treatment were examined under an optical microscope with 3 repetitions. Pollen is considered germinated when pollen tube equaled or exceeded the diameter of the pollen grain. In this study, sucrose was replaced by maltose, and 9 different combinations of pH  and maltose concentration  were tested with an incubation time of 60 min to obtain the optimal combination. Then, the optimal medium was used for pollen germination with different incubation time changes from 15 to 180 min.One representative Japanese variety escribed , consistThe optimized germination medium was used to check the longevity of pollen from the described flowers of the Japanese and Mexican chayote accessions. It was checked after 1, 2, 3, and 7 days of storage at the temperatures 4 and 25 °C. The same method for slide preparation and observation, described in S. edule var. virens levis and S. edule var. albus levis) in Myanmar and Japan were collected from the local markets and farmers’ yards. Photographs were taken to show the approximate morphological features (Mature fruits of the two dominant varieties (features . The obsSolanum tuberosum L.) was utilized as an external standard nuclear DNA content  because its 2C-value was previously reported as 3.2 pg [Two Japanese and five Mexican chayote accessions were used for this part. Experiment reproducibility was assessed through an Analysis of Variance (ANOVA) using the measurement of three repetitions of each accession obtained on different days in the same time period and under equal conditions. The reported tetraploid potato (s 3.2 pg . In addi®) following the Cystain® UV Precise P kit  instructions. The relative 2C-value was computed based on the following formula [The experiment was performed using flow cytometry equipment (Partec formula :Sample 2p < 0.05).The estimated genome size (EGS) was calculated using 1 pg = 978 Mbp . SignifiThe root tips were treated using the following procedure: pretreated in 8-hydroxyquinoline for 4 h at 18 °C, Carnoy’s solution  fixation step for 24–48 h, a maceration in 1N HCl for 5 min at 55 °C and stored in 70% aqueous ethanol. The slides were prepared using standard squash technique and aceto-carmine staining. The observations were made with light microscope, Olympus BX53 .Fruit skin material of 8 Myanmar accessions was dehydrated with silica gel and kept dry until DNA isolation. For 5 Mexico and 8 Japan accessions, young leaf material was sampled and kept at −80 °C. DNA was isolated using modified CTAB method . DNA comFrom the ten SSR primer sets by Machida-Hirano , six shoPolyacrylamide gel electrophoresis (PAGE) was carried out at 100V, 15mA in 1x TBE buffer for 2 h. Thereafter, the gel was scanned and bands were identified with Gel Doc XR Imaging System . An example of results is shown in ® and processed with a plug-in named GeneAlEx 6.503 to calculate Shannon’s information index (I), number of observed alleles (Na), number of effective alleles (Ne), expected heterozygosity (He), and observed heterozygosity (Ho); Wright’s Fst values (Fst) and Nei’s genetic distances (D) were also calculated [K was set from 1 to 10 with 10 iterations [K) using Evanno’s method, which detects the strongest level of population subdivision [K were aligned using CLUMPP 1.1.2 [Q) were plotted with R package ggplot [The co-dominant data were recorded in Microsoft Excellculated ,62. STRUerations ,64,65,66division ,68. All PP 1.1.2 . Hierarce ggplot  and ggtre ggplot ,72,73. Pe ggplot ,62.