Flooding Performance and Optimization of Injection Parameters of Nanosized Oil Displacement Agents in Extra-Low-Permeability Reservoirs.

There have been few studies on the role of nanofluids in oil displacement and injection parameters, despite their significant impact on the oil displacement effect. To enhance oil recovery in an ultralow-permeability reservoir, the nanosized oil-displacement agent with nano-SiO2 modified by a silane coupling agent as a main component was selected for the first time in the Changqing oilfield. To assess the performance of the nanofluid, various factors such as particle size, contact angle, interfacial tension, and emulsion stability were taken into consideration. The oil displacement effect of nanofluids was evaluated by a microscopic model and ultralow-permeability core displacement experiment, and its optimal injection parameters were determined. The average particle size of the nano-oil displacement agent is 22-30 nm. It can change the wetting condition of the rock from oil-wet to water-wet and reduce the oil-water interfacial tension. Even at 80 °C, the emulsion formed by the agent remained stable. The oil displacement experiment shows that the nano-oil displacement agent whose injection pressure increases can displace the residual oil trapped in small pores that cannot be affected by conventional water flooding. The injection mode of "nanoflooding agent drive + water drive + nanoflooding agent drive", injection rate of 0.1 mL/min, injection concentration of 0.5%, and injection volume of 0.5 PV (0.25 PV per segment), which can effectively guide the injection of the oil displacement agent, achieve the best oil displacement effect.

Genome-wide association analysis of type II resistance to Fusarium head blight in common wheat.

Background: Fusarium head blight (FHB) is a disease affecting wheat spikes caused by some Fusarium species and leads to cases of severe yield reduction and seed contamination. Identifying resistance genes/QTLs from wheat germplasm may help to improve FHB resistance in wheat production. Methods: Our study evaluated 205 elite winter wheat cultivars for FHB resistance. A high-density 90K SNP array was used for genotyping the panel. A genome-wide association study (GWAS) from cultivars from three different environments was performed using a mixed linear model (MLM). Results: Sixty-six significant marker-trait associations (MTAs) were identified (P < 0.001) on fifteen chromosomes that explained the phenotypic variation ranging from 5.4 to 11.2%. Some important new MTAs in genomic regions involving FHB resistance were found on chromosomes 2A, 3B, 5B, 6A, and 7B. Six MTAs at 92 cM on chromosome 7B were found in cultivars from two different environments. Moreover, there were 11 MTAs consistently associated with diseased spikelet rate and diseased rachis rate as pleiotropic effect loci and D_contig74317_533 on chromosome 5D was novel for FHB resistance. Eight new candidate genes of FHB resistance were predicated in wheat in this study. Three candidate genes, TraesCS5D02G006700, TraesCS6A02G013600, and TraesCS7B02G370700 on chromosome 5DS, 6AS, and 7BL, respectively, were perhaps important in defending against FHB by regulating intramolecular transferase activity, GTP binding, or chitinase activity in wheat, but further validation in needed. In addition, a total of five favorable alleles associated with wheat FHB resistance were discovered. These results provide important genes/loci for enhancing FHB resistance in wheat breeding by marker-assisted selection.

Genetic dissection of protein and starch during wheat grain development using QTL mapping and GWAS.

Protein, starch, and their components are important for wheat grain yield and end-products, which are affected by wheat grain development. Therefore, QTL mapping and a genome-wide association study (GWAS) of grain protein content (GPC), glutenin macropolymer content (GMP), amylopectin content (GApC), and amylose content (GAsC) were performed on wheat grain development at 7, 14, 21, and 28 days after anthesis (DAA) in two environments using a recombinant inbred line (RIL) population of 256 stable lines and a panel of 205 wheat accessions. A total of 29 unconditional QTLs, 13 conditional QTLs, 99 unconditional marker-trait associations (MTAs), and 14 conditional MTAs significantly associated (p < 10-4) with four quality traits were found to be distributed on 15 chromosomes, with the phenotypic variation explained (PVE) ranging from 5.35% to 39.86%. Among these genomic variations, three major QTLs [QGPC3B, QGPC2A, and QGPC(S3|S2)3B] and SNP clusters on the 3A and 6B chromosomes were detected for GPC, and the SNP TA005876-0602 was stably expressed during the three periods in the natural population. The QGMP3B locus was detected five times in three developmental stages in two environments with 5.89%-33.62% PVE, and SNP clusters for GMP content were found on the 3A and 3B chromosomes. For GApC, the QGApC3B.1 locus had the highest PVE of 25.69%, and SNP clusters were found on chromosomes 4A, 4B, 5B, 6B, and 7B. Four major QTLs of GAsC were detected at 21 and 28 DAA. Most interestingly, both QTL mapping and GWAS analysis indicated that four chromosomes (3B, 4A, 6B, and 7A) were mainly involved in the development of protein, GMP, amylopectin, and amylose synthesis. Of these, the wPt-5870-wPt-3620 marker interval on chromosome 3B seemed to be most important because it played an important role in the synthesis of GMP and amylopectin before 7 DAA, in the synthesis of protein and GMP from 14 to 21 DAA, and in the development of GApC and GAsC from 21 to 28 DAA. Using the annotation information of IWGSC Chinese Spring RefSeq v1.1 genome assembly, we predicted 28 and 69 candidate genes for major loci from QTL mapping and GWAS, respectively. Most of them have multiple effects on protein and starch synthesis during grain development. These results provide new insights and information for the potential regulatory network between grain protein and starch synthesis.

Transcriptome Dynamic Analysis Reveals New Candidate Genes Associated with Resistance to Fusarium Head Blight in Two Chinese Contrasting Wheat Genotypes.

In recent years, Fusarium head blight (FHB) has developed into a global disease that seriously affects the yield and quality of wheat. Effective measures to solve this problem include exploring disease-resistant genes and breeding disease-resistant varieties. In this study, we conducted a comparative transcriptome analysis to identify the important genes that are differentially expressed in FHB medium-resistant (Nankang 1) and FHB medium-susceptible (Shannong 102) wheat varieties for various periods after Fusarium graminearum infection using RNA-seq technology. In total, 96,628 differentially expressed genes (DEGs) were identified, 42,767 from Shannong 102 and 53,861 from Nankang 1 (FDR < 0.05 and |log2FC| > 1). Of these, 5754 and 6841 genes were found to be shared among the three time points in Shannong 102 and Nankang 1, respectively. After inoculation for 48 h, the number of upregulated genes in Nankang 1 was significantly lower than that of Shannong 102, but at 96 h, the number of DEGs in Nankang 1 was higher than that in Shannong 102. This indicated that Shannong 102 and Nankang 1 had different defensive responses to F. graminearum in the early stages of infection. By comparing the DEGs, there were 2282 genes shared at the three time points between the two strains. GO and KEGG analyses of these DEGs showed that the following pathways were associated with disease resistance genes: response to stimulus pathway in GO, glutathione metabolism, phenylpropanoid biosynthesis, plant hormone signal transduction, and plant-pathogen interaction in KEGG. Among them, 16 upregulated genes were identified in the plant-pathogen interaction pathway. There were five upregulated genes, TraesCS5A02G439700, TraesCS5B02G442900, TraesCS5B02G443300, TraesCS5B02G443400, and TraesCS5D02G446900, with significantly higher expression levels in Nankang 1 than in Shannong 102, and these genes may have an important role in regulating the resistance of Nankang 1 to F. graminearum infection. The PR proteins they encode are PR protein 1-9, PR protein 1-6, PR protein 1-7, PR protein 1-7, and PR protein 1-like. In addition, the number of DEGs in Nankang 1 was higher than that in Shannong 102 on almost all chromosomes, except chromosomes 1A and 3D, but especially on chromosomes 6B, 4B, 3B, and 5A. These results indicate that gene expression and the genetic background must be considered for FHB resistance in wheat breeding.

Conditional QTL mapping for seed germination and seedling traits under salt stress and candidate gene prediction in wheat.

Breeding new wheat varieties with salt resistance is one of the best ways to solve a constraint on the sustainability and expansion of wheat cultivation. Therefore, understanding the molecular components or genes related to salt tolerance must contribute to the cultivation of salt-tolerant varieties. The present study used a recombinant inbred line (RIL) population to genetically dissect the effects of different salt stress concentrations on wheat seed germination and seedling traits using two quantitative trait locus (QTL) mapping methods. A total of 31 unconditional and 11 conditional QTLs for salt tolerance were identified on 11 chromosomes explaining phenotypic variation (PVE) ranging from 2.01 to 65.76%. Of these, 15 major QTLs were found accounting for more than 10% PVE. QTL clusters were detected on chromosomes 2A and 3B in the marker intervals 'wPt-8328 and wPt-2087' and 'wPt-666008 and wPt-3620', respectively, involving more than one salt tolerance trait. QRdw3B and QSfw3B.2 were most consistent in two or more salt stress treatments. 16 candidate genes associated with salt tolerance were predicted in wheat. These results could be useful to improve salt tolerance by marker-assisted selection (MAS) and shed new light on understanding the genetic basis of salt tolerance in wheat.

Effects of Preharvest Shading on Dynamic Changes in Metabolites, Gene Expression, and Enzyme Activity of Three Tea Types during Processing.

Preharvest shading significantly influences tea flavor. However, little attention has been given to the mechanism of shading on metabolites, genes, and enzymes in the processing of different tea types. Our study identified 1028 nonvolatile metabolites covering 10 subclasses using a widely targeted metabolome. The results show that shading had a greater effect on the compositions of amino acids, flavonoids, and theaflavins in tea leaves. The combined transcriptomics and enzyme activity analysis results indicate that the upregulated expression of asparagine, aspartate, and tryptophan synthesis genes and proteolytic enzymes promoted the accumulation of amino acids. The downregulated enzyme genes resulted in the reduction of nongalloylated catechins and flavonoid glycosides. Simultaneously, the accumulation of TFs in shaded tea was due to the enhanced enzymatic activities of polyphenol oxidase and peroxidase during processing. Theaflavin-3-3'-di-O-gallate was also significantly positively correlated with the antioxidant and hypoglycemic activities of shaded tea. The results contribute to a better understanding of how preharvest treatments influence summer tea quality.

Targeted and Untargeted Metabolomics Profiling of Wheat Reveals Amino Acids Increase Resistance to Fusarium Head Blight.

Fusarium head blight (FHB), a notorious plant disease caused by Fusarium graminearum (F. graminearum), is severely harmful to wheat production, resulting in a decline in grain quality and yield. In order to develop novel control strategies, metabolomics has been increasingly used to characterize more comprehensive profiles of the mechanisms of underlying plant-pathogen interactions. In this research, untargeted and targeted metabolomics were used to analyze the metabolite differences between two wheat varieties, the resistant genotype Sumai 3 and the susceptible genotype Shannong 20, after F. graminearum inoculation. The untargeted metabolomics results showed that differential amino acid metabolic pathways existed in Sumai 3 and Shannong 20 after F. graminearum infection. Additionally, some of the amino acid contents changed greatly in different cultivars when infected with F. graminearum. Exogenous application of amino acids and F. graminearum inoculation assay showed that proline (Pro) and alanine (Ala) increased wheat resistance to FHB, while cysteine (Cys) aggravated the susceptibility. This study provides an initial insight into the metabolite differences of two wheat cultivars under the stress of F. graminearum. Moreover, the method of optimization metabolite extraction presents an effective and feasible strategy to explore the understanding of the mechanisms involved in the FHB resistance.

Identification of novel genomic regions associated with nine mineral elements in Chinese winter wheat grain.

BACKGROUND: Mineral elements are important for maintaining good human health besides heavy metals. Mining genes that control mineral elements are paramount for improving their accumulation in the wheat grain. Although previous studies have reported some loci for beneficial trace elements, they have mainly focused on Zn and Fe content. However, little information is available regarding the genetic loci differences in dissecting synchronous accumulation of multiple mineral elements in wheat grains, including beneficial and heavy elements. Therefore, a genome-wide association study (GWAS) was conducted on 205 wheat accessions with 24,355 single nucleotide polymorphisms (SNPs) to identify important loci and candidate genes for controlling Ca, Fe, Zn, Se, Cu, Mn, Cd, As, and Pb accumulation in wheat grains. RESULTS: A total of 101 marker-trait associations (MTAs) (P < 10-5) loci affecting the content of nine mineral elements was identified on chromosomes 1B, 1D, 2A, 2B, 3A, 3B, 3D, 4A, 4B, 5A, 5B, 5D, 6B, 7A, 7B, and 7D. Among these, 17 major MTAs loci for the nine mineral elements were located, and four MTAs loci (P < 10-5) were found on chromosomes 1B, 6B, 7B, and 7D. Eight multi-effect MTAs loci were detected that are responsible for the control of more than one trait, mainly distributed on chromosomes 3B, 7B, and 5A. Furthermore, sixteen candidate genes controlling Ca, Fe, Zn, Se, Cd, and Pb were predicted, whose functions were primarily related to ion binding, including metals, Fe, Ca, Cu, Mg, and Zn, ATP binding, ATPase activity, DNA binding, RNA binding, and protein kinase activity. CONCLUSIONS: Our study indicated the existence of gene interactions among mineral elements based on multi-effect MTAs loci and candidate genes. Meanwhile this study provided new insights into the genetic control of mineral element concentrations, and the important loci and genes identified may contribute to the rapid development of beneficial mineral elements and a reduced content of harmful heavy metals in wheat grain.

Genome wide association study of the whiteness and colour related traits of flour and dough sheets in common wheat.

Flour whiteness and colour are important factors that influence the quality of wheat flour and end-use products. In this study, a genome wide association study focusing on flour and dough sheet colour using a high density genetic map constructed with 90K single nucleotide polymorphism arrays in a panel of 205 elite winter wheat accessions was conducted in two different locations in 2 years. Eighty-six significant marker-trait associations (MTAs) were detected for flour whiteness and the brightness index (L* value), the redness index (a* value), and the yellowness index (b* value) of flour and dough sheets (P < 10-4) on homologous group 1, 2, 5 and 7, and chromosomes 3A, 3B, 4A, 6A and 6B. Four, three, eleven, eleven MTAs for the flour whiteness, L* value, a* value, b* value, and one MTA for the dough sheet L* value were identified in more than one environment. Based on MATs, some important new candidate genes were identified. Of these, two candidate genes, TraesCS5D01G004300 and Gsp-1D, for BS00000020_51 were found in wheat, relating to grain hardness. Other candidate genes were associated with proteins, the fatty acid biosynthetic process, the ketone body biosynthetic process, etc.

Influence of Lifestyles on Mild Cognitive Impairment: A Decision Tree Model Study.

OBJECTIVE: To explore the effects of different lifestyle choices on mild cognitive impairment (MCI) and to establish a decision tree model to analyse their predictive significance on the incidence of MCI. METHODS: Study participants were recruited from geriatric and physical examination centres from October 2015 to October 2019: 330 MCI patients and 295 normal cognitive (NC) patients. Cognitive function was evaluated by the Mini-Mental State Examination Scale (MMSE) and Clinical Dementia Scale (CDR), while the Barthel Index (BI) was used to evaluate life ability. Statistical analysis included the χ 2 test, logistic regression, and decision tree. The ROC curve was drawn to evaluate the predictive ability of the decision tree model. RESULTS: Logistic regression analysis showed that low education, living alone, smoking, and a high-fat diet were risk factors for MCI, while young age, tea drinking, afternoon naps, social engagement, and hobbies were protective factors for MCI. Social engagement, a high-fat diet, hobbies, living condition, tea drinking, and smoking entered all nodes of the decision tree model, with social engagement as the root node variable. The importance of predictive variables in the decision tree model showed social engagement, a high-fat diet, tea drinking, hobbies, living condition, and smoking as 33.57%, 27.74%, 22.14%, 11.94%, 4.61%, and 0%, respectively. The area under the ROC curve predicted by the decision tree model was 0.827 (95% CI: 0.795~0.856). CONCLUSION: The decision tree model has good predictive ability. MCI was closely related to lifestyle; social engagement was the most important factor in predicting the occurrence of MCI.

Characterization of QTLs for Seedling Resistance to Tan Spot and Septoria Nodorum Blotch in the PBW343/Kenya Nyangumi Wheat Recombinant Inbred Lines Population.

Tan spot (TS) and Septoria nodorum blotch (SNB) induced by Pyrenophora tritici-repentis and Parastagonospora nodorum, respectively, cause significant yield losses and adversely affect grain quality. The objectives of this study were to decipher the genetics and map the resistance to TS and SNB in the PBW343/Kenya Nyangumi (KN) population comprising 204 F6 recombinant inbred lines (RILs). Disease screening was performed at the seedling stage under greenhouse conditions. TS was induced by P. tritici-repentis isolate MexPtr1 while SNB by P. nodorum isolate MexSN1. Segregation pattern of the RILs indicated that resistance to TS and SNB in this population was quantitative. Diversity Array Technology (DArTs) and simple sequence repeats (SSRs) markers were used to identify the quantitative trait loci (QTL) for the diseases using inclusive composite interval mapping (ICIM). Seven significant additive QTLs for TS resistance explaining 2.98 to 23.32% of the phenotypic variation were identified on chromosomes 1A, 1B, 5B, 7B and 7D. For SNB, five QTLs were found on chromosomes 1A, 5A, and 5B, explaining 5.24 to 20.87% of the phenotypic variation. The TS QTL on 1B chromosome coincided with the pleiotropic adult plant resistance (APR) gene Lr46/Yr29/Pm39. This is the first report of the APR gene Lr46/Yr29/Pm39 contributing to TS resistance.

Genomewide association study for seeding emergence and tiller number using SNP markers in an elite winter wheat population.

Seeding emergence and tiller number are the most important traits for wheat (Triticum aestivum L.) yield, but the inheritance of seeding emergence and tillering is poorly understood. We conducted a genomewide association study focussing on seeding emergence and tiller number at different growth stages with a panel of 205 elite winter wheat accessions. The population was genotyped with a high-density Illumina iSelect 90K SNPs assay. A total of 31 loci were found to be associated with seeding emergence rate (SER) and tiller number in different growth stages. Loci distributed among 12 chromosomes accounted for 5.35 to 11.33% of the observed phenotypic variation. With this information, 10 stable SNPs were identified for eventual development of cleaved amplified polymorphic sequence markers for SER and tiller number in different growth stages. Additionally, a set of elite alleles were identified, such as Ra_c14761_1348-T, which may increase SER by 13.35%, and Excalibur_c11045_236-A and BobWhite_c8436_391-T, which may increase the rate of available tillering by 14.78 and 8.47%, respectively. These results should provide valuable information for marker-assisted selection and parental selection in wheat breeding programmes.

Discovery of Consistent QTLs of Wheat Spike-Related Traits under Nitrogen Treatment at Different Development Stages.

Spike-related traits such as spike length (Sl), fertile spikelet number (Fsn), sterile spikelet number (Ssn), grain number per spike (Gns), and thousand-kernel weight (Tkw) are important factors influencing wheat yield. However, reliably stable markers that can be used for molecular breeding in different environments have not yet been identified. In this study, a double haploid (DH) population was used for quantitative trait locus (QTL) mapping of five spike-related traits under four different nitrogen (N) supply dates in two locations and years. Seventy additive QTLs with phenotypic variation ranging from 4.12 to 34.74% and 10 major epistatic QTLs were identified. Eight important chromosomal regions on five chromosomes (1B, 2B, 2D, 5D, and 6A) were found. Sixteen stable QTLs were detected for which N application had little effect. Among those stable QTLs, QSl.sdau-2D-1, and QSl.sdau-2D-2, with phenotypic variation explained (PVE) of 10.4 and 24.2%, respectively, were flanked by markers Xwmc112 and Xcfd53 in the same order. The QTLs QSsn.sdau-2B-1, QFsn.sdau-2B-1, and QGns.sdau-2B, with PVE ranging from 4.37 to 28.43%, collocated in the Xwmc179-Xbarc373 marker interval. The consistent kernel wheat QTL (QTkw.sdau-6A) on the long arm of chromosome 6A, flanked by SSR markers Xbarc1055 and Xwmc553, showed PVE of 5.87-15.18%. Among these stable QTLs, the two flanking markers Xwmc112 and Xcfd53 have been validated using different varieties and populations for selecting Sl. Therefore, these results will be of great value for marker-assisted selection (MAS) in breeding programs and will accelerate the understanding of the genetic relationships among spike-related traits at the molecular level.

Analysis of diversity and linkage disequilibrium mapping of agronomic traits on B-genome of wheat.

Association mapping is an efficient method to test the association between molecular markers and quantitative trait loci (QTL) based on linkage disequilibrium (LD). In this study, 13 agronomic traits of 109 wheat accessions were evaluated at Tai'an of China in 2006-2010. Genetic diversity, population structure, and LD were investigated using Diversity Array Technology (DArT) markers. The extent of LD on B-genome (chromosomes 1B, 2B, 3B, 4B, 5B, 6B and 7B) was about 18-27 cM. The polymorphism information content (PIC) value of markers in the LD blocks was often lower than the mean value of each chromosome. Analysis of the phenotypic diversity of the 13 traits showed that the population structure accounted for an average of 5.82% of the phenotypic variation. Association of 139 DArT markers on chromosome 1B-7B with the 13 traits was analyzed with a mixed linear model. A total of 84 significant marker trait associations (MTAs) were found and some of the associated markers were located in the QTL region detected in previous linkage mapping studies. Combined with hitchhiking effects, we identified five important markers for future analysis, such as wPt-1708(4B, 93.8cM), wPt-3457(5B, 92.3cM), wPt-9613(5B, 94.4cM), wPt-4858(6B, 66.1cM) and wPt-8598(7B, 142.4cM). The information obtained in this study should be useful for marker-assisted selection in wheat breeding programs.

Unconditional and conditional QTL analysis of kernel weight related traits in wheat (Triticum aestivum L.) in multiple genetic backgrounds.

Wheat thousand kernel weight (TKW) is a complex trait, and is largely controlled by several kernel traits, including kernel length (KL) and kernel width (KW). In order to reveal the genetic relationship between TKW and these kernel traits (KW and KL) as accurate as possible, we applied both unconditional and conditional mapping analyses to three distinct genetic populations, one DH population and two RIL populations. This report describes the identifications of 36 unconditional and conditional additive QTLs and 30 pairs of unconditional and conditional epistatic QTLs, all of which are closely associated with TKW. While the conditional additive locus Qtkw1B, detected in the RIL2 population, exhibited the largest contribution, explaining 14.12 % of TKW variance, the unconditional epistatic QTLs Qtkw3A-2/Qtkw5B.1, detected in the DH population, accounted for 11.95 % of phenotypic variance. This study also showed that, compared with unconditional mapping, conditional mapping resulted in very different numbers and different extent of effects of additive and epistatic QTLs that were associated with TKW when TKW was conditioned on kernel traits (KW and KL). These data strongly suggest that KW and KL indeed play a significant role in determining TKW. Furthermore, we demonstrated that the effects of the 25 additive QTLs for TKW were either entirely or largely determined by KW, while the effects of the other 25 additive QTLs for TKW were either entirely or largely affected by KL. We conclude that the conditional mapping can be useful for a better understanding of the interrelationship between the yield contributing traits at the QTL level.

Genetic dissection reveals effects of interaction between high-molecular-weight glutenin subunits and waxy alleles on dough-mixing properties in common wheat.

The glutenin and waxy loci of wheat are important determinants of dough quality. This study was conducted to evaluate the effects of high-molecular-weight glutenin (HMW-GS) and waxy alleles on dough-mixing properties. Molecular mapping was used to investigate these effects on Mixograph properties in a population of 290 (Nuomai1 x Gaocheng8901) recombinant inbred lines (RILs) from three environments in the harvest years 2008, 2009 and 2011. The results indicated the following: (i) the Glu-A1 and Glu-D1 loci have greater impacts on Mixograph properties compared to the Wx-1 loci and the effects of Glu-D1d and Glu-D1h on dough mixing are better than those of Glu-D1f and Glu-D1new1 in this population; (ii) the interactions between the Glu-1 and Wx-1 loci affected some traits, especially the midline peak value (MPV), and the lack of Wx-B1 or Wx-D1 led to increased MPV for all types of Glu-1 loci; and (iii) 30 quantitative-trait loci (QTL) over nine wheat chromosomes were identified with ICIM analysis based on the genetic map of 498 loci. Eight major QTL and 16 QTL in the Glu-1 loci from the three environments were found. The major QTL clusters were associated with the Glu-1 loci, and also were found in two regions on chromosome 3B and one region on chromosome 6A, which is one of the novel chromosome regions influencing dough-mixing strength. The two QTL for MPV are located around Wx-B1 on chromosome 4A. QMPT-1D.1, QMPI-1D.1 and Q8MW-1D.1 were stable in different environments and could potentially be used in molecular marker-assisted breeding.

Methanol triggered ligand flip isomerization in a binuclear copper(I) complex and the luminescence response.

Methanol drives the blue emissive complex, [Cu(2)(dppy)(3)(MeCN)](BF(4))(2) (dppy = diphenylphosphino-pyridine), with a head-to-tail arrangement of the three bridging phosphine ligands to convert to its linkage isomer (head-to-head, green emissive) in the solid state, and the transformation could be reversibly realized through recrystallization in different solvents.