36 results about "Gene pool" patented technology

This invention discloses an adaptive information distribution resource catalog generation method, apparatus, and product, relating to the field of text generation, to improve catalog generation accuracy and reduce manual intervention workload. The invention includes: information resource feature acquisition; segmenting and encoding the features of each information resource to obtain a resource gene sequence for each information resource; obtaining user behavioral fingerprints; matching each resource gene sequence with a catalog gene pool based on the behavioral fingerprints; if a match is successful, outputting the catalog information of the matched catalog gene sequence; if a match fails, generating a new catalog gene sequence based on the resource gene sequence and updating the catalog gene pool with the new catalog gene sequence. This invention reduces classification error rate through multi-dimensional feature extraction and encoding mechanisms, performs feature matching based on user behavioral fingerprints, adapts to the personalized needs of different users, reduces manual intervention workload, and improves environmental compatibility.

The invention discloses a transcription factor AcWRKY84 gene for regulating and controlling gray mold resistance of kiwi fruits. After the expression of the gene is silenced, the gray mold resistance of kiwi fruits can be remarkably reduced. The AcWRKY84 gene is cloned, and the anti-gray mold effect of the AcWRKY84 gene in kiwi fruits is proved, specifically, after an experimental group is inoculated with viruses, compared with a control group, the disease symptoms of the fruits of the experimental group are aggravated, the infected area is obviously enlarged, and the enzyme activities of SOD, POD and CAT in the fruits are obviously reduced compared with the control group; sufficient gene resources can be provided for a kiwi fruit gray mold resistance gene pool, so that the possibility of successfully breeding high-gray mold-resistance varieties subsequently is greatly increased.

The invention provides a feature gene selection method and system based on deep learning attribution analysis and beam combination optimization, and relates to the technical field of signal analysis, and the method comprises the following steps: obtaining single cell RNA sequencing data and batch RNA sequencing data; the method comprises the following steps: based on single-cell RNA sequencing data, obtaining an initial gene pool aiming at a plurality of disease subtypes through a consensus screening strategy fusing XGBoost multi-dimensional importance measurement and deep learning SHAP attribution analysis; performing first-stage optimization on the gene list of each subtype by taking the initial gene pool as a starting point and adopting beam combination search and based on a global discrimination objective function to obtain a first-stage optimized gene set; integrating the first-stage optimized gene set with the differential expression gene set of the batch RNA sequencing data, and carrying out second-stage optimization by adopting bundle combination search again to obtain a second-stage optimized gene set; and performing cross validation integration on the second-stage optimized gene set, and outputting a final feature gene set.

The present application relates to the field of molecular genetic technology, in particular to a KASP molecular marker TMSK23 related to eggplant reverse temperature-sensitive male sterility and application thereof. The molecular marker site is obtained by crossing 05ms as female parent and S132 as male parent, and obtaining F2 separation population. According to the published high-quality eggplant genome information, the whole genome resequencing technology is used to pool and sequence the homozygous dominant fertile plants and homozygous recessive sterile plants in the F2 generation of eggplant, and the SNP difference sites of the fertile gene and sterile gene pool are screened out, and the difference sites are verified by KASP gene typing method and protein three-dimensional structure prediction. The present application solves the problems of large workload in hybrid seed production in the prior art, avoids the problem of a large number of measurement and matching in the transgenic process, improves the breeding efficiency and has other advantages.

A transcription factor for regulating resistance of kiwifruit to botrytis blight AcWRKY84 The application relates to a gene, and after the expression of the gene is silenced, the resistance of kiwifruit to botrytis blight is significantly reduced. AcWRKY84 The application clones the gene and proves the role of the gene in resisting botrytis blight in kiwifruit, and specifically, after the experimental group is inoculated with the virus, the disease symptoms of the experimental group are more serious, the infected area is obviously increased, and the SOD, POD and CAT enzyme activities in the fruit are significantly reduced compared with those of the control group, so that sufficient gene resources can be provided for a botrytis blight resistance gene library, and the possibility of realizing successful breeding of a high botrytis blight resistance variety is greatly increased.

The invention discloses a power distribution network three-phase imbalance dynamic regulation and control method based on a hybrid intelligent optimization algorithm, and belongs to the field of power system automation. Establishing a dynamic regulation and control model taking minimization of three-phase current unbalance degree and minimization of switch action times as double targets; solving the model based on an improved genetic algorithm fusing an experience knowledge guidance mechanism, a Pareto optimal selection mechanism and a global and local collaborative search mechanism; and generating a commutation control instruction according to the obtained optimal commutation scheme, and issuing the commutation control instruction to a load commutation switch for execution. By introducing a gene pool to guide a breeding process, a multi-target selection mechanism based on non-dominated sorting and a local optimization strategy combined with tabu search, comprehensive improvement of the algorithm in the aspects of convergence speed, solution diversity and global optimality is realized.

The application discloses a method for constructing a digital rock gene library based on massive experimental data, and comprises the following steps: collecting different typical rock samples, carrying out relevant experimental research, obtaining relevant experimental data of the typical rock samples, and processing the relevant experimental data to form rock standardized data; collecting typical geological disaster data occurring in history and processing the geological disaster data to form historical disaster standardized data; collecting real-time dynamic monitoring data of the current geological disaster; and fusing and analyzing the three kinds of heterogeneous data sources and uniformly processing the three kinds of heterogeneous data sources to form the digital rock gene library. The application adopts the three kinds of heterogeneous data sources to construct the digital rock gene library, makes up for defects that data is incomplete and methods are single in the existing rock mechanics field, and leads to different analysis results, has strong broad-spectrum and high accuracy.

The invention relates to a turbulence model optimization method based on gene expression programming, which comprises the following steps: selecting a numerical simulation method in a CFD solver, and calculating vortex viscosity coefficient distribution; obtaining flow field characteristic variables through an RANS turbulence model; inputting a GEP algorithm by taking a vortex viscosity coefficient as a training truth value and a flow field characteristic variable as an input characteristic, carrying out GEP algorithm training work, and generating an initial population by combining three means of chaotic sequence generation, a gene pool predefined structure and random generation; changing individual crossover and mutation probability in real time to obtain a correction formula of the vortex viscosity coefficient; all coefficients in the vortex viscosity coefficient correction formula are optimized to obtain a vortex viscosity coefficient final expression, and the vortex viscosity coefficient final expression is loaded into a CFD solver; in the loaded CFD solver, calculation is carried out through an RANS turbulence model, and needed flow field information is obtained. The method has the capability of giving an explicit model equation, and the calculation precision of the RANS turbulence model is improved.

The invention relates to the technical field of data analysis, in particular to an integrated analysis method and system for multi-source single cell transcriptome data. The method comprises the following steps: converting all gene names of single cell transcriptome data from different sources into a unified version, and filtering source specific genes; a specific background gene pool is constructed, pollution genes are identified, and background RNA pollution is eliminated; calculating the average sequencing depth of each sample cell, and performing sequencing depth correction on each sample cell; based on transcriptome data of single cells from different sources, cell types are identified through cell grouping and characteristic gene expression quantity analysis, and each cell type is obtained; and constructing anchor point cells for each cell type, and correcting the deep equilibrium cell expression profile of the same cell type to obtain an integrated cell expression profile. The accuracy of data integration analysis of the single cell transcriptome is improved, and the influence of batch effect caused by multiple sources on other subsequent analysis is reduced.

The application discloses a kind of control parameter adjustment method and system of magnetic levitation air supply machine, it is related to high-precision control technical field, including, construct information fusion model, the electromechanical thermal coupling characteristic input information fusion model of acquisition, generate electromechanical thermal characteristic vector;With vector decoupling operator, electromechanical thermal characteristic vector is separated into electromechanical thermal characteristic component, and energy stable scalar value is generated in combination with real-time magnetic levitation air supply machine operation data;Distributed digital gene library is constructed, energy stable scalar value is mapped into discrete working condition label, discrete working condition label and gene fragment in distributed digital gene library are matched, and optimal gene fragment is output;Analysis optimal gene fragment generates control parameter set, and generates multiple parameter change trajectory by smoothing operation function group.The application is by constructing distributed digital gene library, so that control parameter self-adapting adaptation continuous change operating condition and equipment aging Slow time-varying factor, significantly improve system robustness.

This invention discloses a prognostic model and construction method for cervical squamous cell carcinoma, belonging to the fields of bioinformatics and oncology. The construction method is based on a set of genes related to programmed cell death, integrating multi-omics data of cervical squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the Cancer Genome Characterization Project (CGCI). After standardized preprocessing, differential expression analysis, bootstrap resampling combined with univariate Cox regression for initial screening, consensus screening using multiple machine learning algorithms, and finally, multivariate Cox proportional hazards regression analysis to identify three core independent prognostic genes and construct a risk scoring model. This model has undergone multi-dimensional validation and optimization, demonstrating robust predictive performance. The method of this invention is standardized and highly reproducible, and the constructed model has high accuracy and strong generalization ability, providing a reliable tool for individualized prognostic assessment and clinical decision-making for cervical squamous cell carcinoma patients.

A method and an apparatus for updating a cloud platform are provided. The method may include: in response to receiving a cloud platform update request, acquiring to-be-used updating cluster gene information corresponding to the cloud platform update request, wherein the to-be-used updating cluster gene information is structured information that completely represents information of components in a cloud platform container cluster; updating a standard cloud platform gene library according to a comparison result between the to-be-used updating cluster gene information and cluster gene information of a current standard cloud platform, wherein the standard cloud platform is a unified cloud platform on which a customer-specific cloud platform is deployed; and determining, according to an updated standard cloud platform gene library, a hardware resource of a final standard cloud platform and a software resource of the final standard cloud platform.

The invention discloses an intelligent manufacturing system and method for a text travel digital cultural product based on a large model, and relates to the technical field of text travel digitization, and the system comprises a data graph module which receives and processes multi-source data, and constructs a text travel knowledge graph; the semantic extraction module is used for finely tuning the model to extract semantic tags, and calculating and verifying the tag reliability; the gene pool matching module is used for retrieving a gene pool and matching visual features with cultural relic parameters; the double-drive dynamic generation module is used for generating a static model and simulating dynamic evolution in combination with semantic vision; the achievement verification output module is used for converting the data into multimedia, verifying the similarity and judging the achievement; according to the invention, through semantic-vision dual-drive dynamic generation and multi-dimensional verification of a closed loop, it is ensured that digital products fit historical logic of cultural relics; meanwhile, through multi-source data integration, semantic optimization and gene pool accurate matching, full-process data and semantic optimization is achieved, and the problems of semantic vision disjunction, data fragmentation and the like in traditional text travel digitization are solved.

This invention provides a species gene classification system, method, apparatus, electronic device, and storage medium, belonging to the field of integrated circuit technology. It includes: a preprocessing module for converting a first serial current signal corresponding to a gene of a species to be tested into a hash vector representing that gene; a content-addressable memory (MAP) for performing vector-matrix multiplication on the hash vectors to obtain multiple first cumulative currents; the MAP is an RRAM array structure, and its conductance is positively correlated with a reference hash vector, where each reference hash vector represents a gene from a species gene pool; and a first comparison module for comparing the various first cumulative currents to determine the species to which the gene belongs. This invention achieves species gene classification through in-memory computation, reducing data movement and eliminating the need for complex algorithms to correct noise, thereby accelerating system operation, reducing sequencing time, and lowering system energy consumption.

The invention provides a Qiang product generation method and system, computer equipment and a storage medium, and belongs to the field of digital protection and innovative design of intangible cultural heritage, and the method comprises the steps: collecting sheep totem literature records and artificial Qiang handicraft real objects, and extracting a vectorization feature curve and process visual features by using an improved Mask R-CNN model; performing three-level coding on the basis of a rooting theory, and constructing a Qiang culture gene bank containing visual features, semantic features and translation rules; converting the gene bank into a cue word matrix in combination with a shape grammar rule, designing an intention graph and a creative sketch, and inputting the intention graph and the creative sketch into an AIGC model to generate a candidate scheme set; and dynamically adjusting AIGC model parameters through a multi-strategy enhanced escape algorithm, and finally outputting an optimal design element and generating a Qiang product. Accurate extraction and intelligent translation of Qiang culture genes are achieved, culture inheritance accuracy and design innovation efficiency are both considered, and intelligent transformation from protection to innovation of the Qiang culture genes is promoted.

The invention discloses a building model family parameter generation method and system based on AI driving. The method comprises the steps that a building design gene pool driving platform is called to extract and screen building model family calibration parameters; inputting the calibration parameters into a building boundary to represent a structured potential geometric tree diffusion model to generate a geometric feature parameter subset; generating a verification hybrid algorithm verification parameter through a building specification embedded parameter, and marking a parameter which does not accord with the rule; the mark parameters are adjusted and corrected in combination with historical data; integrating the parameters, and performing secondary diffusion through a diffusion model to generate an intermediate parameter set; and mapping the intermediate parameter as a building model family target parameter. The system comprises six units which are sequentially connected and cooperatively work. According to the method and the system, historical design data can be fully utilized, deep fusion of specification and parameter generation is realized, the geometric relevance of parameters is enhanced, the problems of low efficiency and lagging of specification verification in the prior art are solved, and the efficiency and the accuracy of parameter generation are improved.

The invention relates to the technical field of architectural cultural heritage protection and intelligent reconstruction, and discloses an intelligent cave reconstruction method and system based on an architectural genetic algorithm. The intelligent cave reconstruction method based on the architectural genetic algorithm comprises the following steps: collecting and coding cave cultural data by using a multi-modal feature extraction algorithm; constructing a culture gene pool; a behavior perception and analysis technology is applied to carry out systematic acquisition and mode extraction on modern human living behaviors, and a behavior mode library is established; a culture behavior interactive evolution model is constructed based on the knowledge graph technology, and collaborative optimization of the two models is achieved; organic unification of cave culture protection and modern function adaptation is achieved; an accurate digital recording system of the culture genes and the behavior modes is established; the adaptability of the transformation scheme is improved through a symbiotic evolution mechanism; the method has good expansibility and universality, and can be popularized and applied to protection and updating of other traditional buildings.

The invention relates to a construction method of a triple negative breast cancer prognosis model based on a macrophage gene, which sequentially comprises the steps of data acquisition and pretreatment, macrophage characteristic gene capture, consensus clustering and gene pool construction, model construction and performance evaluation, variable importance sequencing, external verification and clinical value and the like. Through integration of transcriptome data, clinical information and macrophage related gene expression characteristics of a patient, a high-precision, nonlinear and interpretable survival prediction model is constructed, and scientific screening of treatment response crowds is realized. In addition, the invention also relates to application of the prognosis model constructed by the method in preparation of products for predicting prognosis risk, immunotherapy or chemotherapy sensitivity of triple negative breast cancer patients.

The invention discloses a power system network threat gene pool construction method, system and device and a medium, and the method comprises the steps: obtaining a spatial dimension feature and a time dimension feature, and fusing the spatial dimension feature and the time dimension feature to obtain a unified spatial-temporal feature representation; precipitating the unified spatial-temporal feature representation into a standardized threat gene unit, performing versioning management on the threat gene unit, and constructing a multi-key index to obtain a threat gene pool; and performing directional sample generation by using a generation model under the condition of the threat gene unit, performing quality screening on the generated samples by using a density estimation model, increasing a minority class of threat genes in the threat gene pool, realizing construction of the threat gene pool of the power system network, supplementing long tails, avoiding distribution distortion and mode collapse, and improving the threat gene pool construction efficiency. And the gain is clear and can be reproduced.

The invention provides an agricultural machine full-coverage path planning method based on a fusion ant colony genetic algorithm, and the method comprises the steps: 1, constructing a two-dimensional grid map in a simulation environment, and carrying out the subregion division and merging on the two-dimensional grid map; 2, planning an optimal traversal sequence of the sub-regions by adopting a fused ant colony genetic algorithm which comprises the steps of initializing parameters, establishing a gene pool, constructing a path, updating pheromones, updating the gene pool, iterating and terminating; 3, completing the internal path coverage of the sub-regions by adopting a concentric-square-shaped spiral path coverage mode, and completing the path connection between the sub-regions based on an A * path search algorithm; and step 4, generating a final agricultural machine full-coverage path through a sub-region optimal traversal sequence planned by fusing the ant colony genetic algorithm in the step 2, sub-region internal path coverage in the step 3 and sub-region path connection based on an A * path search algorithm. According to the method, the optimal path in full-coverage path planning can be effectively found.

The present application relates to the technical field of artificial intelligence driven digital marketing, especially to an AI-based new media intelligent marketing method, comprising: step 1: cross-platform user behavior fusion modeling; step 2: multi-modal content gene library construction: analyzing the visual focus area and text emotional tendency of historical content, generating content gene vectors; step 3: dynamic content generation and real-time optimization: retrieving the gene library according to the space-time behavior matrix to generate initial marketing content; real-time monitoring of user interaction behavior, dynamically replacing the elements of the visual focus area and adjusting the emotional intensity of the text; step 4: closed-loop strategy optimization: calculating the dynamic effect index by fusing multi-platform indicators, triggering the gene mutation mechanism when the index continuously decreases, and generating cross-platform compensation content based on the failed user behavior. By analyzing the cross-platform behavior and content preferences of users, the user demand can be accurately predicted, and the content more in line with the individualized preferences of users can be provided.

The application discloses a variable-scale neural network construction method and device based on a learning gene pool, which comprises the following steps: selecting a pre-trained large-scale ancestor network; compressing the key representation capability of the ancestor network into multiple auxiliary networks of different scales through a knowledge distillation method; extracting network modules in the auxiliary networks as learning gene instances to form a learning gene pool; selecting multiple learning gene instances for splicing to construct a variable-scale offspring network; and training or fine-tuning the learning gene pool and the spliced offspring network to obtain a neural network model satisfying different resource constraint conditions. The application only needs to store a single ancestor network and a small amount of learning gene instances, thereby significantly reducing storage costs; meanwhile, smaller-scale neural networks can be generated to adapt to low-resource application scenarios. The application has obvious advantages in model accuracy, resource utilization efficiency and model construction flexibility, and effectively supports rapid deployment of neural networks in multiple scenarios.

The invention relates to the technical field of crop breeding, and discloses an efficient breeding method for directionally improving comprehensive characters of winter wheat, which comprises the following steps: step (1) cross parent selection and preliminary gene polymerization; (2) early generation gene pool construction and high-pressure disease resistance screening; (3) high generation selection and homozygous acceleration; (4) identifying the eurytopic adaptability of multiple ecological regions; and (5) carrying out directional selection on agronomic characters in the whole growth period. According to the method, a single-spike continuous selection strategy is adopted in F2-F4 generations, compared with traditional single plant selection, more inter-plant variation can be contained, gene differences in the same plant can be reserved, a breeding material gene pool is enriched, recessive excellent gene loss caused by only paying attention to external forms is avoided through collaborative screening of agronomic phenotypic characters, early stress resistance and quality preliminary screening, and the breeding quality is improved. The polymerization probability of high-yield, high-quality, disease-resistant and other target genes is remarkably improved, the pain point of waste of early generation gene resources in the traditional breeding technology is effectively solved, and a solid gene foundation is laid for multi-character collaborative improvement; f5 and above ear row / single plant rapid homozygous stable new materials and guarantee the uniformity of offspring, the breeding efficiency can be improved, and provincial and above regional tests can be participated 2-3 years in advance.

The invention relates to the technical field of big data and digital advertisements, and particularly discloses an advertisement putting effect optimization method based on big data. According to the method, an advertisement creative gene pool is constructed, copywriting, color matching, layout and background music are coded into gene segments, and iterative evolution and self-optimization of advertisement materials are achieved through creative population initialization, dynamic feedback evaluation, natural selection, gene cross recombination and an environment pressure driven mutation mechanism. The system updates fitness scores in real time based on multi-source user behavior data, dynamically adjusts mutation probability in combination with external environment factors, and introduces a high-quality gene pool to accelerate convergence and ensure compliance. According to the technical scheme, creative fatigue is delayed, the novelty and conversion efficiency of advertisements are improved, and double optimization of the marketing effect and the user experience is achieved.

The invention discloses a communication data leakage detection and protection method combined with deep learning, and relates to the technical field of communication data security, and the method comprises the following steps: step 1) constructing a protocol semantic gene pool, step 2) constructing a semantic gene driven dynamic fingerprint self-verification model, and step 3) constructing an ambiguity self-resolution protection closed loop. According to the method, the problems that in the prior art, hidden data leakage caused by protocol semantic ambiguity cannot be recognized and utilized and protection and communication compatibility cannot be achieved at the same time are solved by utilizing a protocol semantic genetic analysis and semantic gene driven dynamic fingerprint self-verification model and ambiguity self-resolution protection closed loop cooperation technology; the high-efficiency protection effects of precise hidden leakage recognition, safety and compatibility cooperation and technical system self-evolution are achieved, and the safety operation and maintenance cost of an enterprise is reduced.

The application relates to the technical field of decoration gene library construction, and particularly discloses a Yuan blue and white decoration gene library construction and AI derivative design system and method, the method comprises the following steps: carrying out decoration feature extraction on a surface graph, partitioning the surface graph according to the decoration feature extraction result, identifying each region based on AI, and outputting an identification result; the partitioning result and the identification result of the surface graph of each sample are counted, a sample sample is created; the sample samples are compared, the information acquisition sequence is determined, sample information is acquired, and a decoration gene library is constructed; the surface of a sample model is identified based on AI, an identification result is obtained, the sample is classified according to the identification process and the identification result, the importance of each sample is determined according to the classification result, the information acquisition sequence is further determined, the information of more important samples is acquired earlier, and the data quality of the gene library is improved, so that the gene library can be put into use earlier.

The present application relates to the technical field of data analysis, in particular to a multi-source single-cell transcriptome data integration analysis method and system. The method comprises the following steps: converting all gene names of different source single-cell transcriptome data into a unified version, filtering source-specific genes; constructing a specific background gene pool and identifying contaminant genes to eliminate background RNA contamination; calculating the average sequencing depth of each sample cell and correcting the sequencing depth of each sample cell; based on different source single-cell transcriptome data, cell type identification is carried out through cell clustering and characteristic gene expression analysis, and each cell type is obtained; for each cell type, an anchor cell is constructed, and the expression profile of the depth-balanced cell of the same cell type is corrected to obtain the integrated cell expression profile. The accuracy of single-cell transcriptome data integration analysis is improved, and the influence of batch effect caused by multi-source on subsequent other analysis is reduced.

The invention belongs to the technical field of gene engineering, and particularly relates to a rice ALKe gene and application. The invention provides a rice ALKe gene, the nucleotide sequence of the ALKe gene is shown as SEQ ID NO: 1, and the rice gelatinization temperature gene ALKe is generated by recombination of low-gelatinization-temperature ALKb and high-gelatinization-temperature ALKc2 and is a first allelic gene of a recombination type in the ALK gene. Compared with Huanghuazhan, the near-isogenic line has the advantages that the gelatinization temperature can be slightly increased, the gel consistency is obviously increased, the taste value is increased, and the rice quality is improved. The invention provides a new genetic resource for rice quality improvement.

The invention provides a migration method and device for cross-warehouse scheduling, equipment and a medium. The method comprises the following steps: mining multi-dimensional collected data of a source warehouse to obtain a candidate strategy gene set; performing fitness evaluation on the candidate strategy gene set to obtain a verified strategy gene pool; performing environment fingerprint construction on the parameters of the target warehouse to obtain an environment fingerprint vector; carrying out inheritance screening on the environment fingerprint vector and the verified strategy gene pool to obtain a candidate inheritance gene set; adjusting the candidate inheritance gene set to obtain a localized scheduling strategy set; and in the target warehouse, generating a migration control instruction according to the localized scheduling strategy set and executing the migration control instruction. By implementing the embodiment of the invention, the source warehouse is mined and evaluated, and the environment fingerprint of the target warehouse is constructed, inherited, screened and adjusted to obtain the localized scheduling strategy set, the migration control instruction is generated in the target warehouse according to the localized scheduling strategy set and executed, migration with flexibility, quantifiability and screening verification is realized, and the migration efficiency is improved. The migration efficiency is improved.

The invention discloses a network threat gene pool construction and similarity comparison method, system and device and a medium, and the method comprises the steps: obtaining network flow data, carrying out the feature extraction of the network flow data, and obtaining a multi-dimensional feature vector; performing standardization processing on the multi-dimensional feature vector to obtain a first multi-dimensional feature vector; performing numerical coding conversion on the first multi-dimensional feature vector, and storing and querying the converted first multi-dimensional feature vector to obtain a new sample; obtaining a sample in a network threat gene pool, and performing similarity calculation on the sample and the new sample to obtain a similarity result; the network threat gene pool is updated according to the similarity result, construction and similarity comparison of the network threat gene pool are achieved, and an efficient, intelligent and self-adaptive network security defense solution is jointly formed.