Sequence Selectivity Research Articles

Real-world experience of lenvatinib-based therapy in patients with advanced hepatocellular carcinoma.

Given the significant advancements in the management of hepatocellular carcinoma (HCC) and the emergence of novel treatment approaches, establishing reliable predictors has become crucial for optimizing patient selection and therapeutic sequencing in HCC. In this study, we aimed to investigate the prognostic factors and treatment efficacy associated with lenvatinib-based therapy. We retrospectively enrolled 53 patients receiving lenvatinib monotherapy, and 19 patients receiving lenvatinib plus immune checkpoint inhibitor combination therapy as their first-line systemic treatment for unresectable HCC at a single medical center. We employed univariate and multivariate Cox regression analyses to ascertain the factors influencing survival in these cohorts. For lenvatinib monotherapy and the combination therapy, the objective response rates were 30.2% and 63.2%, respectively (P=0.03); the median progression-free survival (PFS) durations were 7 months [95% confidence interval (CI): 4.5-9.5] and 12 months (95% CI: 6.4-17.6), respectively (P=0.74); and the median overall survival (OS) was not reached in either group (P=0.93). Although patients receiving the combination therapy had a greater treatment response, no significant survival differences were observed between the lenvatinib monotherapy and combination therapy subgroups, even after inverse probability of treatment weighting (IPTW). Patients who received lenvatinib monotherapy could be stratified based on a combination of albumin-bilirubin (ALBI) grade (either grade 1 or 2a) and a neutrophil-lymphocyte ratio (NLR) of ≤5.8. Compared to the other subgroups combined, those who met both of these criteria exhibited PFS with a hazard ratio (HR) of 0.382 (95% CI: 0.168-0.871; P=0.02), corresponding to 11 and 5 months, respectively; and an OS (HR: 0.198, 95% CI: 0.043-0.920; P=0.04) of not reached versus 12 months, respectively, according to multivariate Cox regression analysis. In our study cohort, there were no statistically significant differences observed in the survival rates between patients treated with lenvatinib monotherapy and those treated with a combination of lenvatinib and immunotherapy. The incorporation of ALBI grade and NLR facilitates the stratification of survival outcomes in patients with unresectable HCC undergoing lenvatinib monotherapy.

Preamble Design and Detection Based on Sequence Selection Pattern for Random Access in Non-Terrestrial Networks

Preamble Design and Detection Based on Sequence Selection Pattern for Random Access in Non-Terrestrial Networks

Optimizing Road–Rail Multimodal Transport Schedule for Emergency Response with Congestion and Transshipment Sequence Selection

The current literature mainly uses hub capacity or transport route selection to manage the congestion of emergency multimodal transport and pays less attention to transshipment scheduling. This paper proposes an integrated optimization problem of transport routes and transshipment sequences (ITRTSP) and constructs a hybrid flow shop scheduling model to describe it. Based on this model, a recursive method is proposed to calculate the minimum waiting times that cargoes consume in queues at hubs, given the transport routes and transshipment sequences. Furthermore, a memetic algorithm is designed with route selection as the outer layer and transshipment sequence selection as the inner layer for solving ITRTSP. Compared with existing achievements, the model and algorithms can quantify the dependency between transshipment sequence selection and emergency transport time in multimodal transport network settings. The model and algorithms are applied to solve some real-scale examples and compared with the first-come-first-served (FCFS) rule commonly used in the current literature. The results indicate that the makespan is reduced by up to approximately 4.2%, saving 33.68 h. These findings demonstrate that even with given hub capacities and transport routes, congestion can still be managed and the schedule optimized through transshipment scheduling, further improving emergency transport efficiency.

Subthalamic nucleus neurons encode syllable sequence and phonetic characteristics during speech.

Speech is a complex behavior that can be used to study unique contributions of the basal ganglia to motor control in the human brain. Computational models suggest that the basal ganglia encode either the phonetic content or the sequence of speech elements. To explore this question, we investigated the relationship between phoneme and sequence features of a spoken syllable triplet and the firing rate of subthalamic nucleus (STN) neurons recorded during the implantation of deep brain stimulation (DBS) electrodes in individuals with Parkinson's disease. Patients repeated aloud a random sequence of three consonant-vowel (CV) syllables in response to audio cues. Single-unit extracellular potentials were sampled from the sensorimotor STN; a total of 227 unit recordings were obtained from the left STN of 25 subjects (4 females). Of these, 113 (50%) units showed significant task-related increased firing and 53 (23%) showed decreased firing (t test relative to inter-trial period baseline, P < 0.05). Linear regression analysis revealed that both populations of STN neurons encode phoneme and sequence features of produced speech. Maximal phoneme encoding occurred at the time of phoneme production, suggesting efference copy- or sensory-related processing, rather than speech motor planning (-50 ms and +175 ms relative to CV transition for consonant and vowel encoding, respectively). These findings demonstrate that involvement of the basal ganglia in speaking includes separate single unit representations of speech sequencing and phoneme selection in the STN.NEW & NOTEWORTHY Speech is a unique human behavior that requires dynamic execution of precisely timed and coordinated movements, resulting in intelligible vocalizations. Here, we demonstrate that activity of individual neurons in the subthalamic nucleus (STN) of the basal ganglia encode syllable sequence order and phoneme identity during a speech production task. These findings advance our understanding of neural substrates of human speech and shed light on potential involvement of the STN in complex human behaviors.

Recurrent cortical networks encode natural sensory statistics via sequence filtering.

Recurrent neural networks can generate dynamics, but in sensory cortex it has been unclear if any dynamic processing is supported by the dense recurrent excitatory-excitatory network. Here we show a new role for recurrent connections in mouse visual cortex: they support powerful dynamical computations, but by filtering sequences of input instead of generating sequences. Using two-photon optogenetics, we measure neural responses to natural images and play them back, finding inputs are amplified when played back during the correct movie dynamic context- when the preceding sequence corresponds to natural vision. This sequence selectivity depends on a network mechanism: earlier input patterns produce responses in other local neurons, which interact with later input patterns. We confirm this mechanism by designing sequences of inputs that are amplified or suppressed by the network. These data suggest recurrent cortical connections perform predictive processing, encoding the statistics of the natural world in input-output transformations.

Mechanistic model for epigenetic maintenance by methyl-CpG-binding domain proteins.

DNA methylation is a fundamental element of epigenetic regulation that is governed by the MBD protein superfamily, a group of "readers" that share a highly conserved methyl-CpG-binding domain (MBD) and mediate chromatin remodeler recruitment, transcription regulation, and coordination of DNA and histone modification. Previous work has characterized the binding affinity and sequence selectivity of MBD-containing proteins toward palindromes of 5-methylcytosine (5mC) containing 5mCpG dinucleotides, often referred to as single symmetrically methylated CpG sites. However, little is known about how MBD binding is influenced by the prototypical local clustering of methylated CpG sites and the presence of DNA structural motifs encountered, e.g., during DNA replication and transcription. Here, we use Single-Molecule Kinetics through Equilibrium Poisson Sampling (SiMKEPS) to measure precise binding and dissociation rate constants of the MBD of human protein MBD1 to DNAs with varying patterns of multiple methylated CpG sites and diverse structural motifs. MBD binding is promoted by two major properties of its DNA substrates: 1) tandem (consecutive) symmetrically methylated CpG sites in double-stranded DNA and secondary structures in single-stranded DNA; and 2) DNA forks. Based on our findings, we propose a mechanistic model for how MBD proteins contribute to epigenetic boundary maintenance between transcriptionally silenced and active genome regions.

A study on the development of O2O operation service platform using augmented reality

An attempt of sequence selection is made for multiple alignments of transmembrane proteins in order to detect the structural similarity in the protein families. Treatment for the sequence selection, which is based on the pairwise sequence identities, is applied to ten sequence data sets of functional group of transmembrane proteins extracted from SWISS-PROT. The treatment excludes the sequences with low sequence identity from the divergent data sets effectively. Obtained multiple alignments are evaluated using two newly developed indices. The selected sequences, which are well aligned with few inserted gaps, seem to contain enough information for extracting the structural features of transmembrane functional groups.

Optimization of magnetic resonance imaging of the hand

BACKGROUND: Magnetic resonance imaging is one of the leading imaging modalities of the musculoskeletal system. However, when imaging the hand, major problems in magnetic resonance imaging include the lack of specialized coils and reliable fixation devices for the hand, uncomfortable patient posture, motion artifacts, and small anatomical structures in the wrist. These factors inevitably lead to incorrect interpretation. AIM: To improve the quality of magnetic resonance imaging of the hand by developing an approach to coil selection, scanning protocol, and hand positioning and fixation. MATERIALS AND METHODS: A positioning device was developed to prevent hand movements. Two types of coils were evaluated. Magnetic resonance images were evaluated comparatively, as well as by a musculoskeletal radiologist. RESULTS: А head coil is more appropriate when scanning the entire hand, for example, in rheumatic diseases. A knee coil is more appropriate when studying smaller anatomical structures (including the wrist) owing to a smaller field of view and higher resolution. Based on the obtained data, guidelines for the selection of scanning parameters, sequences, and coils for magnetic resonance imaging of the hand were formulated. To prevent motion artifacts, a special fixation device of the patient’s hand was introduced. CONCLUSION: Certain factors directly affect the qualitative magnetic resonance imaging study of the hand, such as safety protocols, scanning parameters, and hand fixation. The guidelines presented in this study and the use of the developed specialized fixation device may improve the quality of magnetic resonance imaging of the hand.

Advancements in Lentil Genomics for Enhanced Crop Breeding: A Review

Lentil (Lens culinaris Medik) is an essential pulse crop that is widely grown for its high nutritional value, notably its high protein content, making it an important dietary component for vegetarians and vegans. Despite being the world's fifth most produced pulse, with large contributions from Canada and India, lentil production confronts obstacles such as poor productivity due to limited genetic improvement against biotic and abiotic stresses under rainfed cultivation conditions. Recent advances in lentil genetics and genomics, such as the discovery of genes related to yield, disease resistance, and nutritional content, have boosted breeding efforts to generate improved lentil varieties. The use of contemporary genomic techniques like molecular markers, marker-assisted selection (MAS), genomic selection (GS), and next-generation sequencing (NGS) technology has sped up the discovery of quantitative trait loci (QTLs) and the production of novel cultivars with superior agronomic characteristics. Databases such as NCBI and ENA, as well as specialized resources like KnowPulse, provide critical genomic data, while the creation of lentil genome assemblies, notably the CDC Redberry variety, has improved our understanding of lentil genetics. These resources help to solve the constraints of traditional breeding, particularly for complex characteristics impacted by genotype-environment interactions, opening the way for more robust and productive lentil varieties. Although the application of advanced tools such as genetic engineering, cisgenesis, and genome editing has moved more slowly in lentils than in other crops, their potential to improve lentil output is encouraging. Recent studies on lentil genomes, together with the creation of increased genetic resources and cutting-edge techniques, offer the ability to overcome production constraints and dramatically increase lentil production and quality throughout the world.

AARS Online: A collaborative database on the structure, function, and evolution of the aminoacyl-tRNA synthetases.

The aminoacyl-tRNA synthetases (aaRS) are a large group of enzymes that implement the genetic code in all known biological systems. They attach amino acids to their cognate tRNAs, moonlight in various translational and non-translational activities beyond aminoacylation, and are linked to many genetic disorders. The aaRS have a subtle ontology characterized by structural and functional idiosyncrasies that vary from organism to organism, and protein to protein. Across the tree of life, the 22 coded amino acids are handled by 16 evolutionary families of Class I aaRS and 21 families of Class II aaRS. We introduce AARS Online, an interactive Wikipedia-like tool curated by an international consortium of field experts. This platform systematizes existing knowledge about the aaRS by showcasing a taxonomically diverse selection of aaRS sequences and structures. Through its graphical user interface, AARS Online facilitates a seamless exploration between protein sequence and structure, providing a friendly introduction to the material for non-experts and a useful resource for experts. Curated multiple sequence alignments can be extracted for downstream analyses. Accessible at www.aars.online, AARS Online is a free resource to delve into the world of the aaRS.

Evaluating metagenomics and targeted approaches for diagnosis and surveillance of viruses

BackgroundMetagenomics is a powerful approach for the detection of unknown and novel pathogens. Workflows based on Illumina short-read sequencing are becoming established in diagnostic laboratories. However, high sequencing depth requirements, long turnaround times, and limited sensitivity hinder broader adoption. We investigated whether we could overcome these limitations using protocols based on untargeted sequencing with Oxford Nanopore Technologies (ONT), which offers real-time data acquisition and analysis, or a targeted panel approach, which allows the selective sequencing of known pathogens and could improve sensitivity.MethodsWe evaluated detection of viruses with readily available untargeted metagenomic workflows using Illumina and ONT, and an Illumina-based enrichment approach using the Twist Bioscience Comprehensive Viral Research Panel (CVRP), which targets 3153 viruses. We tested samples consisting of a dilution series of a six-virus mock community in a human DNA/RNA background, designed to resemble clinical specimens with low microbial abundance and high host content. Protocols were designed to retain the host transcriptome, since this could help confirm the absence of infectious agents. We further compared the performance of commonly used taxonomic classifiers.ResultsCapture with the Twist CVRP increased sensitivity by at least 10–100-fold over untargeted sequencing, making it suitable for the detection of low viral loads (60 genome copies per ml (gc/ml)), but additional methods may be needed in a diagnostic setting to detect untargeted organisms. While untargeted ONT had good sensitivity at high viral loads (60,000 gc/ml), at lower viral loads (600–6000 gc/ml), longer and more costly sequencing runs would be required to achieve sensitivities comparable to the untargeted Illumina protocol. Untargeted ONT provided better specificity than untargeted Illumina sequencing. However, the application of robust thresholds standardized results between taxonomic classifiers. Host gene expression analysis is optimal with untargeted Illumina sequencing but possible with both the CVRP and ONT.ConclusionsMetagenomics has the potential to become standard-of-care in diagnostics and is a powerful tool for the discovery of emerging pathogens. Untargeted Illumina and ONT metagenomics and capture with the Twist CVRP have different advantages with respect to sensitivity, specificity, turnaround time and cost, and the optimal method will depend on the clinical context.

Optimal Utilization of Battery Sources in Cascaded H-Bridge Inverter by Coordinated Sequence Selection

Optimal Utilization of Battery Sources in Cascaded H-Bridge Inverter by Coordinated Sequence Selection

Phase sequence selection method of double-circuit transmission lines on the same tower based on a comprehensive index

Abstract The non-transposition erection of transmission lines will lead to the imbalance of electrical parameters, which will affect the protection and automatic reclosing. The best phase sequence arrangement is determined by comprehensively considering the comprehensive indexes (current imbalance, secondary current, and recovery voltage) of double transmission un-transposed lines with shared towers. PSCAD/EMTDC electromagnetic transient simulation software is applied to build a typical 750kV transmission line model for simulation analysis, and the comprehensive indexes under different phase sequence arrangements are studied. The analysis reveals that the arrangement of phase sequence exerts a significant consequence of the comprehensive index. The findings indicate that the margin and different secondary current and recovery voltage indexes, double transmission un-transposed lines with shared towers can be erected in the same direction or in different phase sequences to reduce the current imbalance.

A multi-objective genetic algorithm based on two-stage reinforcement learning for green flexible shop scheduling problem considering machine speed

The consumption of energy and resources in the manufacturing industry has garnered significant attention due to the increasingly severe environmental issues. Green shop scheduling research is focused on optimizing economic and environmental indicators within the current manufacturing model. This paper specifically addresses the flexibility of job-shop scheduling problem considering machine speed (CMS-FJSP), as different machine speeds during the production process can impact energy and resource consumption. The objectives of this study include minimizing the makespan, total energy consumption, and tool wear. To tackle this problem, a multi-objective genetic algorithm that incorporates a two-stage reinforcement learning approach is proposed. In light of the characteristics of the problem, a three-layer encoding approach is suggested, which encompasses machine allocation, operation sequencing, and machine speed selection. Additionally, a decoding method that integrates energy-saving strategies is proposed to enhance the optimization process. To improve the quality of the population, three distinct initialization methods have been developed. Furthermore, a parameter adjustment strategy informed by two-stage reinforcement learning is introduced. This strategy incorporates a state set and action set tailored to the unique characteristics of two-stage reinforcement learning, alongside corresponding reward mechanisms. In 30 test cases, the proposed algorithm demonstrates superior uniformity and convergence compared to five classical algorithms. In a practical case within a hydraulic component production workshop conducted at a hydraulic component company, the proposed algorithm generates 26 scheduling schemes with different focuses, achieving a 14.39% reduction in makespan, a 2.13% decrease in energy consumption, and a 10.65% reduction in tool wear.

Diversifying selection identified in immune epitopes of bovine coronavirus isolates from Irish cattle.

Bovine betacoronavirus (BoCoV) is a pneumoenteric pathogen of cattle that is closely related to human coronavirus OC43. Vaccines are administered to protect against diseases caused by BoCoV, but knowledge gaps exist with regard to correlates of protection and the effect of immune evasion on driving evolution. In this study, immune epitopes were mapped onto BoCoV structural proteins, including spike and haemagglutinin esterase (HE), and then supported with targeted gene sequencing of Irish clinical isolates and selective pressure analysis. Increased prevalence of diversifying selection and amino acid changes in some mapped immune epitopes suggests that immune escape is selecting for non-synonymous mutations arising in these regions. Selection analysis and sequencing provided increased support for neutralising antibody (nAb) epitopes compared to others, suggesting that nAbs are an important arm of the immune response to BoCoV. Phylogenetic analysis of spike and HE sequences showed that Irish isolates from this study were in the European clade, except for one HE sequence that sat in the Asian/American clade, while the spike gene of this sample was in the European clade. Recombination between a European and an Asian/American isolate would give rise to such a sequence. This study has gathered evidence suggesting that pressure to evade the nAb response is contributing to BoCoV evolution.

Integrated precise orbit determination of GPS, BDS, and partial satellites selected from LEO constellation

Traditional precise orbit determination (POD) for Global Navigation Satellite System (GNSS) typically relies on a considerable number of global ground stations. The rapid development of large Low Earth Orbit (LEO) communication constellations in recent years presents an opportunity for integrated POD of LEO and GNSS satellites. The participation of abundant observations from onboard GNSS receivers in GNSS POD can significantly enhance the orbit precision, availability, and integrity of GNSS satellites. However, the improvement in orbit precision comes with a notable increase in computation time when a substantial number of LEO satellites are involved. In this study, we propose a method for selecting LEO satellites based on the LEO geometric dilution of precision (LGDOP) value. By identifying the LEO satellite combination corresponding to the minimum LGDOP, partial LEO satellites evenly distributed are selected from the entire LEO constellation. Using the semi-realistic simulation approach, only onboard observations of the LEO constellation are simulated to enhance GPS and BDS POD. The average accuracy of orbit determination for simulated LEO satellites is 2.27 cm in 3D directions, comparable to FengYun-3 but inferior to GRACE-FO. When onboard observations from 10 LEO satellites, selected using the minimum LGDOP method, participate in the integrated POD, the orbit precision can be improved by 10 %-14 % compared with the sequence selection method. Consequently, the LGDOP selection method optimizes the distribution of LEO satellites and demonstrates superior effectiveness in enhancing GPS and BDS POD with an equivalent number of LEO satellites. Involving 20–30 LEO satellites in the solution can enhance the orbit precision of GPS and BDS satellites by approximately 40 % and 80 % for global and regional ground stations, respectively. Increasing the number of participating LEO satellites further does not significantly improve the GPS and BDS orbit precision but does notably increase the computation time, which exceed 12 h for regional stations and 14 h for global stations. Thus, selecting approximately 20–30 LEO satellites using the proposed method effectively balances orbit determination precision and computation efficiency.

Allele-Selective Thiomorpholino Antisense Oligonucleotides as a Therapeutic Approach for Fused-in-Sarcoma Amyotrophic Lateral Sclerosis.

Pathogenic variations in the fused in sarcoma (FUS) gene are associated with rare and aggressive forms of amyotrophic lateral sclerosis (ALS). As FUS-ALS is a dominant disease, a targeted, allele-selective approach to FUS knockdown is most suitable. Antisense oligonucleotides (AOs) are a promising therapeutic platform for treating such diseases. In this study, we have explored the potential for allele-selective knockdown of FUS. Gapmer-type AOs targeted to two common neutral polymorphisms in FUS were designed and evaluated in human fibroblasts. AOs had either methoxyethyl (MOE) or thiomorpholino (TMO) modifications. We found that the TMO modification improved allele selectivity and efficacy for the lead sequences when compared to the MOE counterparts. After TMO-modified gapmer knockdown of the target allele, up to 93% of FUS transcripts detected were from the non-target allele. Compared to MOE-modified AOs, the TMO-modified AOs also demonstrated reduced formation of structured nuclear inclusions and SFPQ aggregation that can be triggered by phosphorothioate-containing AOs. How overall length and gap length of the TMO-modified AOs affected allele selectivity, efficiency and off-target gene knockdown was also evaluated. We have shown that allele-selective knockdown of FUS may be a viable therapeutic strategy for treating FUS-ALS and demonstrated the benefits of the TMO modification for allele-selective applications.

Site-Selective Functionalization of Molecularly Imprinted Nanoparticles to Recognize Lysine-Rich Peptides.

Sequence-selective binding of peptides has been a long-standing goal of chemists. As one of the most abundant amino acids in proteins, lysine plays an important role in protein functions as well as in antimicrobial and cell-penetrating peptides. Herein, we report molecularly imprinted nanoparticles (NPs) with high sequence selectivity for lysine-rich peptides. The NPs are prepared from molecular imprinting of cross-linkable surfactant micelles and postmodification of the imprinted pockets by photoaffinity labeling. The method allows carboxylic acids to be installed precisely near the lysine amino side chains, greatly enhancing the binding strengths of lysine-rich peptides. Small variations in the peptide sequence can be distinguished, and the binding affinity correlates positively with the number of lysine groups in model tripeptides. The method applies to complex lysine-rich biological peptides, achieving hundreds of nanomolar binding affinities and excellent binding specificities.

Comparison of SNCG and NEFH Promoter-Driven Expression of Human SIRT1 Expression in a Mouse Model of Glaucoma.

Adeno-associated virus (AAV) demonstrates promise in delivering therapeutic genes to retinal ganglion cells (RGCs). Delivery of neuroprotective genes is constrained by packaging size and/or cell selectivity. This study compares the ability of the RGC-selective gamma-synuclein (SNCG) promoter and the smaller RGC-selective neurofilament heavy chain (NEFH) promoter, as well as portions of the RGC-selective atonal bHLH transcription factor 7 (ATOH7) enhancer, to drive gene expression in RGCs. AAV2 constructs with green fluorescent protein (GFP) or human sirtuin 1 (hSIRT1) driven by cytomegalovirus (CMV) enhancer and NEFH promoter (AAV2-eCMV-NEFH) or distal active sequences of the ATOH7 enhancer (DiATOH7) with the SNCG promoter (AAV2-DiATOH7-SNCG) were intravitreally injected into C57BL/6J mice. RGCs were immunolabeled with Brn3a antibodies and counted. AAV constructs with the utmost transduction efficiency were used to test the therapeutic efficacy of the hSIRT1 gene in 12-week-old C57BL/6J mice subjected to microbead (MB)-induced intraocular pressure (IOP) elevation. Visual function was measured using optokinetic responses (OKRs). The eGFP transduction efficiency of AAV2-eCMV-NEFH was similar to that of AAV2-eCMV-SNCG and AAV2-DiATOH7-SNCG. When combined with the SNCG promoter, a larger ATOH7 enhancer was less efficient than the shorter DiATOH7 enhancer. Similarly, the hSIRT1 efficiency of AAV2-eCMV-NEFH was similar to that of AAV2-eCMV-SNCG. The latter two vectors were equally efficient in increasing RGC survival and improving visual function in the mouse model of MB-induced IOP elevation. SNCG and NEFH promoters represent two equally efficient and comparable RGC selective promoter sequences; however, the NEFH promoter offers a smaller packaging size. Smaller enhancer-promoter combinations can be used to deliver larger genes in human cells and for treatment in optic neuropathies including glaucoma.

Hybrid model for sentiment analysis combination of PSO, genetic algorithm and voting classification

As social network services like Weibo and Twitter have grown in popularity, natural language processing (NLP) has seen a great deal of interest in sentiment analysis of social media messages and Information mining. Social media users, whose numbers are always increasing, have the ability to exchange information on their platforms. The study of sentiment, domains and themes are closely related. Manually collecting enough labelled data from the vast array of subjects covered by large-scale social media to train sentiment classifiers across several domains would be extremely difficult. The literature review conducted concludes that models already proposed in the previous researches are not able to achieve good accuracy. This work suggests a unique model that combines of genetic algorithm and particle swarm optimization to effectively extract the features and then the voting technique is applied for the classification. Model proposed is compared with 4 ensemble datasets achieving a consistent accuracy of more than 90% for three different diversified database owing to natural selection of sequences by GA and at the same time achieves a fast convergence with PSO, the model may be employed for highly accurate recommenders demanding precision and accuracy.