GeneMatcher Research Articles

State-specific Regulation of Electrical Stimulation in the Intralaminar Thalamus of Macaque Monkeys: Network and Transcriptional Insights into Arousal.

Long-range thalamocortical communication is central to anesthesia-induced loss of consciousness and its reversal. However, isolating the specific neural networks connecting thalamic nuclei with various cortical regions for state-specific anesthesia regulation is challenging, with the biological underpinnings still largely unknown. Here, simultaneous electroencephalogram-fuctional magnetic resonance imaging(EEG-fMRI) and deep brain stimulation are applied to the intralaminar thalamus in macaques under finely-tuned propofol anesthesia. This approach led to the identification of an intralaminar-driven network responsible for rapid arousal during slow-wave oscillations. A network-based RNA-sequencing analysis is conducted of region-, layer-, and cell-specific gene expression data from independent transcriptomic atlases and identifies 2489 genes preferentially expressed within this arousal network, notably enriched in potassium channels and excitatory, parvalbumin-expressing neurons, and oligodendrocytes. Comparison with human RNA-sequencing data highlights conserved molecular and cellular architectures that enable the matching of homologous genes, protein interactions, and cell types across primates, providing novel insight into network-focused transcriptional signatures of arousal.

Isolation and characterization of normal mandibular periosteal stem cells from human and macaca mulatta and cross-species single-cell analysis

Objective: To investigate the presence of a distinct stem cell populations different from mesenchymal stem cells in the mandibular periosteum of both human and non-human primates (macaca mulatta), to explore its properties during intramembranous osteogenesis and to establish standard protocols for the isolation, culturing and expanding of mandibular periosteal stem cells (PSC) distinguished from other PSCs in other anatomical regions. Methods: Periosteum was harvested from the bone surface during flap bone removal in patients aged 18-24 years undergoing third molar extraction and from the buccal side of the mandibular premolar region of 6-year-old macaca mulatta respectively, and then subjected to single-cell sequencing using the Illumina platform Novaseq 6000 sequencer. Cross-species single-cell transcriptome sequencing results were compared using homologous gene matching. PSC were isolated from primary tissues using two digestion methods with body temperature and low temperature, and their surface markers (CD200, CD31, CD45 and CD90) were identified by cell flow cytometry. The ability of cell proliferation and three-lineage differentiation of PSC expanded to the third generation in vitro in different species were evaluated. Finally, the similarities and differences in osteogenic properties of PSC and bone marrow mesenchymal stem cells (BMSC) were compared. Results: The single-cell sequencing results indicated that 18 clusters of cell populations were identified after homologous gene matching for dimensionality reduction, and manual cellular annotation was conducted for each cluster based on cell marker databases. The comparison of different digestion protocols proved that the low-temperature overnight digestion protocol can stably isolate PSC from the human and m. mulatta mandibular periosteum and the cells exhibited a fibroblast-like morphology. This research confirmed that PSC of human and m. mulatta had similar proliferation capabilities through the cell counting kit-8 assay. Flow cytometry analysis was then used to identify the cells isolated from the periosteum expressed CD200(+), CD31(-), CD45(-), CD90(-). Then, human and m. mulatta PSC were induced into osteogenesis, adipogenesis, and chondrogenesis to demonstrate their corresponding multi-lineage differentiation capabilities. Finally, comparison with BMSC further clarified the oesteogenesis characteristics of PSC. The above experiments proved that the cells isolated from the periosteum were peiosteal cells with characteristics of stem cells evidenced by their cell morphology, proliferation ability, surface markers, and differentiation ability, and that this group of PSC possessed characteristics different from traditional mesenchymal stem cells. Conclusions: In this study, normal mandibular PSC from humans and m. mulatta were stably isolated and identified for the first time, providing a cellular foundation for investigating the mechanism of mandibular intramembranous osteogenesis, exploring ideal non-human primate models and establishing innovative strategies for clinically mandibular injury repair.

Etiological involvement of KCND1 variants in an X-linked neurodevelopmental disorder with variable expressivity

Utilizing trio whole-exome sequencing and a gene matching approach, we identified a cohort of 18 male individuals from 17 families with hemizygous variants in KCND1, including two de novo missense variants, three maternally inherited protein-truncating variants, and 12 maternally inherited missense variants. Affected subjects present with a neurodevelopmental disorder characterized by diverse neurological abnormalities, mostly delays in different developmental domains, but also distinct neuropsychiatric signs and epilepsy. Heterozygous carrier mothers are clinically unaffected. KCND1 encodes the α-subunit of Kv4.1 voltage-gated potassium channels. All variant-associated amino acid substitutions affect either the cytoplasmic N- or C-terminus of the channel protein except for two occurring in transmembrane segments 1 and 4. Kv4.1 channels were functionally characterized in the absence and presence of auxiliary β subunits. Variant-specific alterations of biophysical channel properties were diverse and varied in magnitude. Genetic data analysis in combination with our functional assessment shows that Kv4.1 channel dysfunction is involved in the pathogenesis of an X-linked neurodevelopmental disorder frequently associated with a variable neuropsychiatric clinical phenotype.

Establishment and clinical application of the HLA genotype database of platelet-apheresis donors in Suzhou

The establishment of a platelet-apheresis donor database may provide a feasible solution to improve the efficacy of platelet transfusion in patients with immune platelet transfusion refractoriness (PTR). This study aimed to establish HLA genotype database in Suzhou, to provide HLA-I compatible platelets for PTR patients to ensure the safety and effectiveness of platelet transfusions. We used a polymerase chain reaction sequence-based typing (PCR-SBT) method to establish the database by performing high-resolution HLA-A, -B, and –C genotyping on 900 platelet-apheresis donors. HLA-I antibody was detected in patients using a Luminex device, and HLA-I gene matching was performed by an HLA-Matchmaker. We found that the highest frequency of the HLA-A allele was A*11:01 (17.06 %), followed by A*24:02 (14.67 %) and A*02:01 (13.61 %). The highest frequency of the HLA-B allele was B*46:01 (9.78 %), followed by B*40:01 (8.39 %) and B*13:02 (33 %). After the detection of platelet antibodies in 74 patients with immune PTR, we found 30 HLA-A antibodies and 48 HLA-B antibodies, and there were a variety of high frequency antibodies whose alleles were low in the donor database, such as HLA-A*68:02, and B*57:01. After avoiding donor-specific antibodies (DSA) matching, 102 of 209 platelet-compatible transfusions were effective, resulting in an effective rate of 48.8 %, which significantly improved the efficacy of platelet transfusion. The establishment of a platelet donor database is of great significance to improve the therapeutic effect of platelet transfusion in patients with hematologic disorder, and save blood resources, and it is also the premise and guarantee of precise platelet transfusion.

Postulation of leaf rust resistance genes in Indian and exotic wheat germplasm using near-isogenic lines (NILs) and molecular markers

Leaf rust caused by Puccinia triticina Eriks. (Pt) is one of the most devastating fungal diseases affecting wheat (Triticum aestivum L.) production worldwide. Deployment of resistant cultivars is a prominent eco-friendly approach to manage this disease. But major constraint in this case is emergence of new pathotypes of pathogen due to faster evolution. Widening the gene pool by gene mining from diverse panel is the key strategy to keep pace with the ever-evolving pathogen. So, in this study attempts were made to identify leaf rust resistance genes in a selected set of 149 germplasm lines by seedling reaction test against two most prevalent Pt pathotypes, 77–9 (MHTKL) and 77–5 (THTTM) and for field resistance at Ludhiana and Gurdaspur during 2018–2021. Based on seedling infection type (IT) data, 88 lines were found resistant (low IT; 0–2) and 39 lines were susceptible (high IT; ≥3). Under field conditions, 77 lines showed highly resistance response across the locations and years. Gene postulation in the test germplasm was done by gene matching technique and further confirmed by linked molecular markers; which revealed the presence of 11 Lr genes singly or in combinations. Leaf rust resistance gens; Lr9, Lr19, Lr24, Lr34, Lr37, Lr42, Lr45, Lr52, Lr57, Lr58 and Lr76 were detected in 4, 7, 10, 1, 7, 33, 41, 44, 2, 24 and 6 lines respectively. The present study's resistant germplasm lines can be utilised as resistant parents in crosses intended for pyramiding and the deployment of leaf rust resistance genes.

Cell type matching across species using protein embeddings and transfer learning.

Knowing the relation between cell types is crucial for translating experimental results from mice to humans. Establishing cell type matches, however, is hindered by the biological differences between the species. A substantial amount of evolutionary information between genes that could be used to align the species is discarded by most of the current methods since they only use one-to-one orthologous genes. Some methods try to retain the information by explicitly including the relation between genes, however, not without caveats. In this work, we present a model to transfer and align cell types in cross-species analysis (TACTiCS). First, TACTiCS uses a natural language processing model to match genes using their protein sequences. Next, TACTiCS employs a neural network to classify cell types within a species. Afterward, TACTiCS uses transfer learning to propagate cell type labels between species. We applied TACTiCS on scRNA-seq data of the primary motor cortex of human, mouse, and marmoset. Our model can accurately match and align cell types on these datasets. Moreover, our model outperforms Seurat and the state-of-the-art method SAMap. Finally, we show that our gene matching method results in better cell type matches than BLAST in our model. The implementation is available on GitHub (https://github.com/kbiharie/TACTiCS). The preprocessed datasets and trained models can be downloaded from Zenodo (https://doi.org/10.5281/zenodo.7582460).

Investigation of FRMPD4 variants associated with X-linked epilepsy

BackgroundThe etiology of unexplained epilepsy in most patients remains unclear. Variants of FRMPD4 are suggested to be associated with neurodevelopmental disorders. Therefore, we screened for disease-causing FRMPD4 variants in patients with epilepsy. MethodsTrios-based whole-exome sequencing was conducted on a cohort of 85 patients with unexplained epilepsy, their parents, and extended family members. Additional cases with FRMPD4 variants were identified from the China Epilepsy Gene Matching Platform V.1.0. The frequency of variants was analyzed, and their subregional effects were predicted using in silico tools. The genotype–phenotype correlation of the newly defined causative genes and protein stability were analyzed using I-Mutant V.3.0 and Grantham scores. ResultsTwo novel missense variants of FRMPD4 were identified in two families. Using the gene matching platform, we identified three additional novel missense variants. These variants presented at low or no allele frequencies in the gnomAD database. All the variants were located outside the three FRMPD4 main domains (WW, PDZ, and FERM). In silico analyses revealed that the variants were damaging and were predicted to be the least stable. All patients eventually became seizure-free. Eight of the 21 patients with FRMPD4 variants had epilepsy, of which five (63%) had missense variants located outside the domains, two had deletions involving exon 2, and one had a frameshift variant located outside the domains. Patients with epilepsy caused by missense variants were often free of intellectual disabilities (4/5), whereas patients with epilepsy caused by truncated variants had intellectual disabilities and structural brain abnormalities (3/3). ConclusionsThe FRMPD4 gene is potentially associated with epilepsy. The genotype–phenotype correlation of FRMPD4 variants indicated that differences in variant types and locations of FRMPD4 may explain their phenotypic variation.

Multi-omics identifies large mitoribosomal subunit instability caused by pathogenic MRPL39 variants as a cause of pediatric onset mitochondrial disease.

MRPL39 encodes one of 52 proteins comprising the large subunit of the mitochondrial ribosome (mitoribosome). In conjunction with 30 proteins in the small subunit, the mitoribosome synthesizes the 13 subunits of the mitochondrial oxidative phosphorylation (OXPHOS) system encoded by mitochondrial Deoxyribonucleic acid (DNA). We used multi-omics and gene matching to identify three unrelated individuals with biallelic variants in MRPL39 presenting with multisystem diseases with severity ranging from lethal, infantile-onset (Leigh syndrome spectrum) to milder with survival into adulthood. Clinical exome sequencing of known disease genes failed to diagnose these patients; however quantitative proteomics identified a specific decrease in the abundance of large but not small mitoribosomal subunits in fibroblasts from the two patients with severe phenotype. Re-analysis of exome sequencing led to the identification of candidate single heterozygous variants in mitoribosomal genes MRPL39 (both patients) and MRPL15. Genome sequencing identified a shared deep intronic MRPL39 variant predicted to generate a cryptic exon, with transcriptomics and targeted studies providing further functional evidence for causation. The patient with the milder disease was homozygous for a missense variant identified through trio exome sequencing. Our study highlights the utility of quantitative proteomics in detecting protein signatures and in characterizing gene-disease associations in exome-unsolved patients. We describe Relative Complex Abundance analysis of proteomics data, a sensitive method that can identify defects in OXPHOS disorders to a similar or greater sensitivity to the traditional enzymology. Relative Complex Abundance has potential utility for functional validation or prioritization in many hundreds of inherited rare diseases where protein complex assembly is disrupted.

Genome Sequence of the Endosymbiont Endozoicomonas sp. Strain GU-1 (Gammaproteobacteria), Isolated from the Staghorn Coral Acropora pulchra (Cnidaria: Scleractinia).

Endozoicomonas sp. strain GU-1 was isolated from two separate staghorn coral (Acropora pulchra) colonies collected in Guam, Micronesia. Both isolates were grown in marine broth prior to DNA extraction and Oxford Nanopore Technologies (ONT) sequencing. Genomes were approximately 6.1 Mbp in size, containing highly similar gene content and matching sets of rRNA sequences.

Evaluation of machine learning classifiers for predicting essential genes in Mycobacterium tuberculosis strains.

Accurate investigation and prediction of essential genes from bacterial genome is very important as it might be explored in effective targets for antimicrobial drugs and understanding biological mechanism of a cell. A subset of key features data obtained from 14 genome sequence-based features of 20 strains of Mycobacterium tuberculosis bacteria whose essential gene information was downloaded from ePath and NCBI database for mapping and matching essential genes by using a genome extraction program. The selection of key features was performed by using Genetic Algorithm. For each of three classifiers, 80%, 10% and 10% of subset key features were used for training, validation and testing, respectively. Experimental results (10-f-cv) illustrated that DNN (proposed), DT, and SVM achieved AUC of 0.98, 0.88 and 0.82, respectively. DNN (proposed) outperformed DT and SVM. The higher prediction accuracy of classifiers was observed because of using only key features which also justified better generalizability of classifiers and efficiency of key features related to gene essentiality. Besides, DNN (proposed) also showed best prediction performance while compared with other predictors used in previous studies. The genome extraction program was developed for mapping and matching of essential genes between ePath and NCBI database.

The eighth International Forum on Stem Cells: virtual meeting, October 20-21, 2022.

The eighth International Forum on Stem Cells: virtual meeting, October 20-21, 2022.

Diverse viruses of marine archaea discovered using metagenomics.

During the past decade, metagenomics became a method of choice for the discovery of novel viruses. However, host assignment for uncultured viruses remains challenging, especially for archaeal viruses, which are grossly undersampled compared to viruses of bacteria and eukaryotes. Here, we assessed the utility of CRISPR spacer targeting, tRNA gene matching and homology searches for viral signature proteins, such as major capsid proteins, for the assignment of archaeal hosts and validated these approaches on metaviromes from Yangshan Harbor (YSH). We report 35 new genomes of viruses which could be confidently assigned to hosts representing diverse lineages of marine archaea. We show that the archaeal YSH virome is highly diverse, with some viruses enriching the previously described virus groups, such as magroviruses of Marine Group II Archaea (Poseidoniales), and others representing novel groups of marine archaeal viruses. Metagenomic recruitment of Tara Oceans datasets on the YSH viral genomes demonstrated the presence of YSH Poseidoniales and Nitrososphaeria viruses in the global oceans, but also revealed the endemic YSH-specific viral lineages. Furthermore, our results highlight the relationship between the soil and marine thaumarchaeal viruses. We propose three new families within the class Caudoviricetes for the classification of the five complete viral genomes predicted to replicate in marine Poseidoniales and Nitrososphaeria, two ecologically important and widespread archaeal groups. This study illustrates the utility of viral metagenomics in exploring the archaeal virome and provides new insights into the diversity, distribution and evolution of marine archaeal viruses.

Discovery of enzymes to biotransform ginsenoside Rd into ginsenosides F2 and CK using metagenomics and genomic mining.

Ginsenosides are the main active components of ginseng, including many types and different contents. Among them, minor ginsenosides have better biological functions and pharmacological activities than those of the major ginsenosides. However, minor ginsenosides cannot be obtained in large quantities, but by means of enzymatic transformation technology, some major ginsenosides can be de-glycosylated at a specific position to generate minor ginsenosides. In this study, we report two glycosidase genes associated with the conversion of ginsenoside Rd to ginsenosides F2 or CK. SWMU-CK-1 was identified among the total genes extracted from the feces of plum deer by local Blast screening for putative ginsenoside conversion function, which could cause the conversion of ginsenoside Rd → F2 → CK. The other gene was found in the Bifidobacterium breve 689b SGAir 0764 chromosome genome, which might have the same function as the β-glucosidase gene testified by the gene matching, named SWMU-F2-2, and can achieve the Rd → F2 transformation. This study reports two genes that enable achieving the biotransformation of rare ginsenosides, while it provides a new insight and a promising approach to explore new genes and develop new functions of existing genes.

Secure approximate pattern matching protocol via Boolean threshold private set intersection

Approximate pattern matching (APM) measures whether the Hamming distance between two strings is less than a threshold value. APM has been widely utilized, such as gene matching and facial recognition. Yet, the genetic data are privacy-sensitive, resulting that the owners are unwilling to share the raw data. This inspires us to explore how to securely perform APM. After revisiting threshold private set intersection (TPSI), we first propose and formalize a functionality named Boolean threshold private set intersection (BTPSI). The new proposed BTPSI primitive returns a Boolean value (0 or 1) to the user, rather than the actual elements in TPSI. We then construct a secure protocol for the BTPSI functionality with semihonest security. Besides, we first combine oblivious transfer and BTPSI to achieve the efficient construction of secure approximate pattern matching (SAPM) protocol in a semihonest model. Furthermore, we implement our SAPM protocol to demonstrate its real practicality. The performance result shows that when the text length is 2 20 ${2}^{20}$ and the pattern length is 2 10 ${2}^{10}$ , the total runtime is less than 3 s.

Prediction of Driver Gene Matching in Lung Cancer NOG/PDX Models Based on Artificial Intelligence

Patient-derived tumor xenografts (PDXs) are a powerful tool for drug discovery and screening in cancer. However, current studies have led to little understanding of genotype mismatches in PDXs, leading to massive economic losses. Here, we established PDX models from 53 lung cancer patients with a genotype matching rate of 79.2% (42/53). Furthermore, 17 clinicopathological features were examined and input in stepwise logistic regression (LR) models based on the lowest Akaike information criterion (AIC), least absolute shrinkage and selection operator (LASSO)-LR, support vector machine (SVM) recursive feature elimination (SVM-RFE), extreme gradient boosting (XGBoost), gradient boosting and categorical features (CatBoost), and the synthetic minority oversampling technique (SMOTE). Finally, the performance of all models was evaluated by the accuracy, area under the receiver operating characteristic curve (AUC), and F1 score in 100 testing groups. Two multivariable LR models revealed that age, number of driver gene mutations, epidermal growth factor receptor ( EGFR ) gene mutations, type of prior chemotherapy, prior tyrosine kinase inhibitor (TKI) therapy, and the source of the sample were powerful predictors. Moreover, CatBoost (mean accuracy = 0.960; mean AUC = 0.939; mean F1 score = 0.908) and the eight-feature SVM-RFE (mean accuracy = 0.950; mean AUC = 0.934; mean F1 score = 0.903) showed the best performance among the algorithms. Meanwhile, application of the SMOTE improved the predictive capability of most models, except CatBoost. Based on the SMOTE, the ensemble classifier of single models achieved the highest accuracy (mean = 0.975), AUC (mean = 0.949), and F1 score (mean = 0.938). In conclusion, we established an optimal predictive model to screen lung cancer patients for non-obese diabetic (NOD)/Shi-scid, interleukin-2 receptor (IL-2R) γ null (NOG)/PDX models and offer a general approach for building predictive models.

Methodological Study on the Establishment of HLA/HPA Gene Bank of Platelet Donors and Its Clinical Application.

The current study aimed at establishing a large-scale platelet donor database with known HPA and HLA genotypes in Ningbo, to provide matched platelets for the clinic and prevent the occurrence of ineffective immune platelet transfusion refractoriness (PTR). The platelet banks of clinical donor wtih HLA-A, HLA-B, HPA1, HPA2, HPA3, HPA4, HPA5, HPA6, HPA10, HPA15 and HPA21 genotyps were established. Meanwile, the platelet gene matching was performed on the donor. It was found that there were phenotype polymorphisms in 1000 donnors with HPA1, HPA2, HPA3, HPA4, HPA5, HPA6, HPA10, HPA15 and HPA21 genotyps, and allel polymorphism distribution in donnors with HPA1, HPA2, HPA3, HPA4, HPA5, HPA6, HPA15, HPA21 genotyps. The frequency of HPA10 was a gene, and not b gene, showing a single linear distribution. The HPA 2, HPA3, HPA15 system were the most polymorphic with three phenotypes: aa, ab, bb. In the HLA-A allele, the highest frequency is A*11:01 (24.25%). There were 13 alleles that were greater than 1%, such as A*24:02, A*02:01, A*33:03, and the accumulated frequency reached 96.20%. In the HLA-B allele, the highest gene frequency was B*40:01 (13.40%). There were 24 alleles that were greater than 1%, such as B*46: 01, B*58: 01, B*15: 01, and the accumulated frequency reached 91.60%. Platelet antibody cross matching was performed on 100 blood samples from patients with thrombocytopenia after multiple platelet transfusions. The number of consistent samples was 46 (46%). Twenty patients were transfused with platelet cross matching. Among them, 18 patients had obvious improvement in clinical symptoms and good hemostatic effect after transfusion, which was judged to be effective. Platelet donor HPA and HLA-A, B antigen genotyping database provided patients with individual appropriate platelets, and provided the effectiveness of immune platelet infusion ensuring effective platelet transfusion.

The impact of GeneMatcher on international data sharing and collaboration.

GeneMatcher (genematcher.org) is a tool designed to connect individuals with an interest in the same gene. Now used around the world to create collaborations and generate the evidence needed to support novel disease gene identification, GeneMatcher is a founding member of the Matchmaker Exchange (MME; matchmakerexchange.org) and strongest possible advocate for global data sharing including those in resource‐limited environments. As of October 1, 2021, there are 12,531 submitters from 94 countries who have submitted 58,134 submissions with 13,498 unique genes in the database. Among these genes, 8970 (64%) have matched at least once and the total number of matches is 378,806, growing by about 10,000 per month. GeneMatcher submitters increase by 80–120 each month and submissions grow by >800 per month, while unique genes and gene matches continue to grow steadily at rate of about 80 per month. The number of genes without a match peaked at 4371 in February of 2019 and despite the increase in the number of new submissions, the number of unique genes without a match continues to slowly decline, currently standing at 4,016. All submissions in GeneMatcher are available for matching across the MME.

A clinical laboratory's experience using GeneMatcher-Building stronger gene-disease relationships.

The use of whole-genome sequencing (WGS) has accelerated the pace of gene discovery and highlighted the need for open and collaborative data sharing in the search for novel disease genes and variants. GeneMatcher (GM) is designed to facilitate connections between researchers, clinicians, health-care providers, and others to help in the identification of additional patients with variants in the same candidate disease genes. The Illumina Clinical Services Laboratory offers a WGS test for patients with suspected rare and undiagnosed genetic disease and regularly submits potential candidate genes to GM to strengthen gene-disease relationships. We describe our experience with GM, including criteria for evaluation of candidate genes, and our workflow for the submission and review process. We have made 69 submissions, 36 of which are currently active. Ten percent of submissions have resulted in publications, with an additional 14 submissions part of ongoing collaborations and expected to result in a publication.

SWMQ: Secure wildcard pattern matching with query

Secure wildcard pattern matching (WPM) allows the pattern holder to obtain the matched positions without revealing pattern and text information about both parties. However, standard secure WPM may have limitations in practical applications, as users may prefer to have access to the actual data of the match in many scenarios. Fortunately, secure wildcard pattern matching with query (SWMQ) extends standard secure WPM by allowing the pattern holder to obtain the matched positions and the actual data, which has important applications in many scenarios, such as electronic healthcare and gene matching. This also motivates the research of SWMQ in this paper. In this study, we focus on the efficient construction of SWMQ in the semihonest adversary setting. First, we propose two new primitives, hereafter referred to as shared wildcard pattern matching (Sh-WPM) and choice-sharing oblivious transfer (CSOT). Furthermore, we propose an SWMQ protocol via Shared WPM and CSOT. In addition, we evaluate the performance of SWMQ. More specifically, the running time in local area network and wide area network settings is less than 0.4 and 2 s, respectively, when the text length is 2 16 ${2}^{16}$ and the pattern length is 2 12 ${2}^{12}$ . In fact, our evaluation results suggest that SWMQ is not only more broadly functional, but also comparable in efficiency to state-of-the-art approaches.

Disease Control and Prevention in Rare Plants Based on the Dominant Population Selection Method in Opportunistic Social Networks.

The spread of seeds of rare and dangerous plants affects the regeneration, pattern, genetic structure, invasion, and settlement of plant populations. However, seed transmission is a relatively weak research link. The spread of plant seeds is not controlled by the communicator. Rather, this event results from the interaction between the host and the external environment determined by the mother. The way plants transmit and accept seeds is similar to how user nodes accept data transmission requests in social networks. Plants select the characteristics including seed size, maturity time, and gene matching, which are consistent with the size, delay, and keywords of the data received by the user. In this study, we selected rare and endangered Pterospermum heterophyllum as the research object and applied them to a social network. All plants were considered nodes and all seeds as transmitted data. This method avoids the influence of errors in actual sampling and statistical laws. By using historical information to record the reception of seeds, the Infection and Immunity Algorithm (IAIA) in opportunistic social networks was established. This method selects healthy plants through plant social populations and reduces the number of diseased plants. The experimental results show that the IAIA algorithm has a good effect in distinguishing dominant seedlings from seedlings with disease genes and realizes the selection of dominant plants in social networks.