In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

This paper presents a label-free affinity-based capacitive biosensor using interdigitated electrodes. Using an optimized process of DNA probe preparation to minimize the effect of contaminants in commercial thiolated DNA probe, the electrode surface was functionalized with the 24-nucleotide DNA probes based on the West Nile virus sequence (Kunjin strain). The biosensor has the ability to detect complementary DNA fragments with a detection limit down to 20 DNA target molecules (1.5 aM range), making it suitable for a practical point-of-care (POC) platform for low target count clinical applications without the need for amplification. The reproducibility of the biosensor detection was improved with efficient covalent immobilization of purified single-stranded DNA probe oligomers on cleaned gold microelectrodes. In addition to the low detection limit, the biosensor showed a dynamic range of detection from 1 µL -1 to 10 5 µL -1 target molecules (20 to 2 million targets), making it suitable for sample analysis in a typical clinical application environment. The binding results presented in this paper were validated using fluorescent oligomers.
A Sensitive DNA Capacitive Biosensor Using Interdigitated Electrodes (BIOS-D-16-01739, Revised) Affinity-based Capacitive Biosensor Interdigitated Electrodes Nucleic Acid-based Biosensor Viral Diagnostics Pathogen Detection Point-of-care Diagnostics

Ability of CD8 + T cells to act as cytolytic effectors and produce IFN-γ was shown to mediate resistance to Toxoplasma gondii in murine models due to recognition of peptides restricted by murine MHC Class I molecules. However, no T. gondii specific HLA-B07 restricted peptides were proven protective against T gondii. Recently, two T gondii-specific HLA-B*0702-restricted T cell epitopes, GRA7 20-28 (LPQFATAAT) and GRA3 27-35 (VPFVVFLVA), displayed highaffinity binding to HLA-B*0702, and elicited IFN-γ from PBMCs of seropositive HLA-B*0702 persons. Herein, these peptides were evaluated to determine whether they could elicit IFN-γ in splenocytes of HLA-B*0702 transgenic mice when administered with adjuvants and protect against subsequent challenge. Peptide-specific IFN-γ producing T cells were identified by ELISPOT and proliferation assays utilizing splenic T lymphocytes from HLA transgenic mice. When HLA-B*0702 mice were immunized with one of the epitopes identified, GRA7 20-28 in conjunction with a universal CD4 + T cell epitope (PADRE) and adjuvants (CD4 + T cell adjuvant, GLA-SE, and TLR2 stimulatory Pam 2 Cys for CD8 + T cells), this immunization induced CD8 + T cells to produce IFN-γ and protected mice against high parasite burden when challenged with T gondii. This work demonstrates feasibility of bioinformatics followed by an empirical approach based on HLA binding to test this biological activity for identifying protective HLA-B*0702 restricted T gondii peptides and adjuvants that elicit protective immune responses in HLA-B*0702 mice.
Toxoplasma gondii HLA-B*0702 restricted GRA7 20-28 peptide with adjuvants and an universal helper T cell epitope elicits CD8 + T cells producing IFN-γ and reduces parasite burden in HLA- B*0702 mice 1 NIH Public Access

A larger amount of sequence data in private and public databases produced by next-generation sequencing put new challenges due to limitation associated with the alignment-based method for sequence comparison. so, there is a high need for faster sequence analysis algorithms. In this study, we developed an alignment-free algorithm for faster sequence analysis. the novelty of our approach is the inclusion of fuzzy integral with Markov chain for sequence analysis in the alignment-free model. the method estimate the parameters of a Markov chain by considering the frequencies of occurrence of all possible nucleotide pairs from each DNA sequence. these estimated Markov chain parameters were used to calculate similarity among all pairwise combinations of DNA sequences based on a fuzzy integral algorithm. this matrix is used as an input for the neighbor program in the pHYLIp package for phylogenetic tree construction. our method was tested on eight benchmark datasets and on inhouse generated datasets (18 s rDNA sequences from 11 arbuscular mycorrhizal fungi (AMF) and 16 s rDNA sequences of 40 bacterial isolates from plant interior). The results indicate that the fuzzy integral algorithm is an efficient and feasible alignment-free method for sequence analysis on the genomic scale. Phylogenetic tree analysis and comparative studies of taxa are essential parts of modern molecular biology. Phylogenetic reconstruction and comparative sequence analysis traditionally depend on multiple or pairwise sequence alignments. However, various limitations are encountered when analyzing large datasets using an alignment based approach. Whole genome alignment of higher eukaryotes can exceed computational resources. Moreover, factors such as the combinatorics of genomic rearrangements and duplications make the alignment of entire genomes impossible. Therefore, the alignable homologous segments of the genomes under study have to be identified in the initial steps. Recently, large amounts of sequence data produced by next-generation sequencing techniques have become available in private and public databases, which has created new challenges due to the limitations associated with alignment based approaches. This plethora of sequence information increases the computation and time requirements for genome comparisons in computational biology. Therefore, there is a high need for faster sequence analysis algorithms. For this, various methods have been proposed to overcome the limitations of alignment based approach 1-3 , and is termed as alignment-free methods. The alignment-free methods are not only used in phylogenetic studies 4,5 , but also for metagenomics 6-11 , analysis of regulatory elements 12-14 , protein classification 15,16 , sequence assembly 17 , isoform quantification from transcriptome data 18 , and to identify biomarkers in diagnostic tests 19 . The alignment-free methods fall into two broad categories: methods based on k-mer or word frequency, and methods based on match length 20 . Methods based on k-mer or word frequency are quite popular and studied extensively. The k-mer based methods were developed to compare DNA sequences, in which it counts the frequencies of substrings with k letters occurring in respective sequences 21 . In recent past, a lot of k-mer based methods have been proposed and implemented in sequence analysis and phylogeny, such as, feature frequency profile (FFP) 22 , return time distribution (RTD) 23 , frequency chaos game representation (FCGR) 24 , an improved complete composition vector method (ICCV) 25 , composition vector (CV) 26 and complete composition vector (CCV) 27 . For sequence comparison, ICCV method is more efficient and robust compared to CV and CCV methods. The other category of the alignment-free method is based on match lengths, where it employs the similarity of substrings between two sequences 28-31 . Examples of match length methods are, k-mismatch average common substring 32 , average common substring 28 , K r -method 28 , etc. These methods are commonly used for string processing in computer science. In this study, we propose to use fuzzy integral 33 to analyze DNA sequences based on a Markov chain 34 , which can be categorised as k-mer or word frequency method. The fuzzy integral similarity method 35,36 assigns a similarity score between two DNA sequences based on the estimated parameters of a Markov chain. A DNA sequence consists of four characters (A, T, G and C). By taking the state space as S = {A, T, G, C}, we used the k-th step transition probability matrix, a fuzzy measure 37 and fuzzy integral to describe the DNA sequences. We used the fuzzy integral similarity to obtain a distance matrix, which was used in the neighbor program in the PHYLIP package 38 to construct a phylogenetic tree. The similar fuzzy integral similarity approach was taken by 36 . However in 36 , the method of feature vector extraction from the DNA sequences is different from our method. In both our method and 36 , the extracted features are used as an input for the fuzzy integral similarity analysis. The proposed method is tested on 18S rDNA sequences from 11 Arbuscular mycorrhizal fungi isolates and 16S rDNA sequences from 40 bacterial isolates, and also tested on the following benchmark datasets, 41 mammalian mitochondrial genomes, 59 ebolavirus complete genomes, 30 coronavirus whole genomes, 30 bacterial whole genomes, 48 Hepatitis E virus (HEV) whole genomes, 24 Eutherian mammals sequences, 58 genome datasets from different species and 29 Escherichia/Shigella complete genomes. The method was also tested on large mammalian dataset. In addition, we used receiver operating characteristic (ROC) 39-41 curve for measuring the performance of our method to compare the other alignment-free methods from Alfree repository 2 . The consistency can also be seen from the statistical analysis such as AUC (area under the ROC) values, calculated from ROC curves provided in Supplementary Material.

Alignment-free method for DNA sequence clustering using Fuzzy integral similarity