Sequence Statistical Analysis Research Articles

Sequence-Based Protein Design: A Review of Using Statistical Models to Characterize Coevolutionary Traits for Developing Hybrid Proteins as Genetic Sensors.

Statistical analyses of homologous protein sequences can identify amino acid residue positions that co-evolve to generate family members with different properties. Based on the hypothesis that the coevolution of residue positions is necessary for maintaining protein structure, coevolutionary traits revealed by statistical models provide insight into residue-residue interactions that are important for understanding protein mechanisms at the molecular level. With the rapid expansion of genome sequencing databases that facilitate statistical analyses, this sequence-based approach has been used to study a broad range of protein families. An emerging application of this approach is to design hybrid transcriptional regulators as modular genetic sensors for novel wiring between input signals and genetic elements to control outputs. Among many allosterically regulated regulator families, the members contain structurally conserved and functionally independent protein domains, including a DNA-binding module (DBM) for interacting with a specific genetic element and a ligand-binding module (LBM) for sensing an input signal. By hybridizing a DBM and an LBM from two different family members, a hybrid regulator can be created with a new combination of signal-detection and DNA-recognition properties not present in natural systems. In this review, we present recent advances in the development of hybrid regulators and their applications in cellular engineering, especially focusing on the use of statistical analyses for characterizing DBM-LBM interactions and hybrid regulator design. Based on these studies, we then discuss the current limitations and potential directions for enhancing the impact of this sequence-based design approach.

The structure of an uncertain world: Combining sequences classification and career life histories to identify narrative schemas in the careers of financial journalists

This article argues in favour of combining statistical sequence analysis with the study of biographical narratives to identify the underlying social structures in the unfolding of professional careers in particularly uncertain worlds. The careers of French financial journalists, whose professional world has been characterised by exceptional turbulence since the 1990s, are used as a case study. The article begins by describing the variety of relationships between trajectories and social structures in social science research that uses sequence analysis techniques. It then shows that by involving the interpretation of career life histories at each stage of sequence analysis, it is possible to highlight relevant regularities, even when trajectories unfold in contexts of high uncertainty. For this purpose, an optimal matching technique that calculates the dissimilarity between each pair of sequences is combined with an agglomeration technique that groups similar sequences into classes. The method is applied here to 297 sequences of professional activities of former and current financial journalists, reconstructed from the information they provided in the LinkedIn socio-professional network or in a professional directory. By comparing these regularities to the interpretations made by journalists in their 37 career life histories, the article examines three narrative schemas constructed by journalists to navigate an uncertain world and which have historically followed one another: the vocational schema, the gateway schema, and the decline schema. It thus describes the conditions for the emergence and decline of these schemas, which are linked not only to changing employment opportunities, but also to the ability of journalists to observe the traces left behind by the career paths of their counterparts.

How do learners participate in interaction in different types of discussion topics and learning tools of cMOOCs?

ABSTRACT This study aims to explore learners’ interaction performance in different types of cMOOC discussion topics and learning tools. The discussion topic is the problem context and communication framework, and online forums and blogs are typical learning tools containing discussion topics. This study focused on 5 types of forum discussion topics and 4 types of blog discussion topics in cMOOCs and took 3 dimensions to represent interaction performance. 96 learners enrolled in a cMOOC were recruited. Content analysis, descriptive statistical analysis and lag sequence analysis were used. The results showed that in forum discussion topics, the type of Commenting and Suggesting discussion topics is more likely to attract learners to participate in interactions, the Phenomenon Analysis and Brainstorming are more conducive to deep interaction, and the Feeling Sharing is more conducive to generating diverse interaction patterns. Second, in blog discussion topics, the Feeling Sharing easily attracts learners to continuously participate in high-level interaction, and the Summarizing and Creating is beneficial for promoting deep interaction in every interaction, and both can promote interaction transformation. Finally, the blog learning tool is more conducive to learners’ interaction performance than forums. This study provides empirical evidence for cMOOC designers to understand learners' interaction performance.

Large-scale statistical analysis of Mycobacterium tuberculosis genome sequences identifies compensatory mutations associated with multi-drug resistance

Tuberculosis (TB), caused by Mycobacterium tuberculosis, has a significant impact on global health worldwide. The development of multi-drug resistant strains that are resistant to the first-line drugs isoniazid and rifampicin threatens public health security. Rifampicin and isoniazid resistance are largely underpinned by mutations in rpoB and katG respectively and are associated with fitness costs. Compensatory mutations are considered to alleviate these fitness costs and have been observed in rpoC/rpoA (rifampicin) and oxyR’-ahpC (isoniazid). We developed a framework (CompMut-TB) to detect compensatory mutations from whole genome sequences from a large dataset comprised of 18,396 M. tuberculosis samples. We performed association analysis (Fisher’s exact tests) to identify pairs of mutations that are associated with drug-resistance, followed by mediation analysis to identify complementary or full mediators of drug-resistance. The analyses revealed several potential mutations in rpoC (N = 47), rpoA (N = 4), and oxyR’-ahpC (N = 7) that were considered either ‘highly likely’ or ‘likely’ to confer compensatory effects on drug-resistance, including mutations that have previously been reported and validated. Overall, we have developed the CompMut-TB framework which can assist with identifying compensatory mutations which is important for more precise genome-based profiling of drug-resistant TB strains and to further understanding of the evolutionary mechanisms that underpin drug-resistance.

Exploring the fragmentation efficiency of proteins analyzed by MALDI-TOF-TOF tandem mass spectrometry using computational and statistical analyses.

Matrix-assisted laser desorption/ionization time-of-flight-time-of-flight (MALDI-TOF-TOF) tandem mass spectrometry (MS/MS) is a rapid technique for identifying intact proteins from unfractionated mixtures by top-down proteomic analysis. MS/MS allows isolation of specific intact protein ions prior to fragmentation, allowing fragment ion attribution to a specific precursor ion. However, the fragmentation efficiency of mature, intact protein ions by MS/MS post-source decay (PSD) varies widely, and the biochemical and structural factors of the protein that contribute to it are poorly understood. With the advent of protein structure prediction algorithms such as Alphafold2, we have wider access to protein structures for which no crystal structure exists. In this work, we use a statistical approach to explore the properties of bacterial proteins that can affect their gas phase dissociation via PSD. We extract various protein properties from Alphafold2 predictions and analyze their effect on fragmentation efficiency. Our results show that the fragmentation efficiency from cleavage of the polypeptide backbone on the C-terminal side of glutamic acid (E) and asparagine (N) residues were nearly equal. In addition, we found that the rearrangement and cleavage on the C-terminal side of aspartic acid (D) residues that result from the aspartic acid effect (AAE) were higher than for E- and N-residues. From residue interaction network analysis, we identified several local centrality measures and discussed their implications regarding the AAE. We also confirmed the selective cleavage of the backbone at D-proline bonds in proteins and further extend it to N-proline bonds. Finally, we note an enhancement of the AAE mechanism when the residue on the C-terminal side of D-, E- and N-residues is glycine. To the best of our knowledge, this is the first report of this phenomenon. Our study demonstrates the value of using statistical analyses of protein sequences and their predicted structures to better understand the fragmentation of the intact protein ions in the gas phase.

Four classic "de novo" genes all have plausible homologs and likely evolved from retro-duplicated or pseudogenic sequences.

Despite being previously regarded as extremely unlikely, the idea that entirely novel protein-coding genes can emerge from non-coding sequences has gradually become accepted over the past two decades. Examples of "de novo origination", resulting in lineage-specific "orphan" genes, lacking coding orthologs, are now produced every year. However, many are likely cases of duplicates that are difficult to recognize. Here, I re-examine the claims and show that four very well-known examples of genes alleged to have emerged completely "from scratch"- FLJ33706 in humans, Goddard in fruit flies, BSC4 in baker's yeast and AFGP2 in codfish-may have plausible evolutionary ancestors in pre-existing genes. The first two are likely highly diverged retrogenes coding for regulatory proteins that have been misidentified as orphans. The antifreeze glycoprotein, moreover, may not have evolved from repetitive non-genic sequences but, as in several other related cases, from an apolipoprotein that could have become pseudogenized before later being reactivated. These findings detract from various claims made about de novo gene birth and show there has been a tendency not to invest the necessary effort in searching for homologs outside of a very limited syntenic or phylostratigraphic methodology. A robust approach is used for improving detection that draws upon similarities, not just in terms of statistical sequence analysis, but also relating to biochemistry and function, to obviate notable failures to identify homologs.

Analysis of key roles in large‐scale online learning: Interactive participation characteristics and knowledge construction behaviour patterns

AbstractOnline teacher professional development is a branch of online learning, which offers educators opportunities to refine their pedagogical skills and collaboratively construct knowledge within professional learning communities. During these interactions, teachers often engage in knowledge construction while exhibiting diverse role characteristics. Investigating these role characteristics and knowledge construction patterns can enhance the comprehension of the interaction process and contribute to online teacher professional development design, addressing challenges such as weak activity and low collaboration. This study examined the key roles that actively interact or contribute to the online community in online teacher professional development, analysing interactive participation characteristics and knowledge construction behaviour patterns. Data were collected from an online teacher professional development activity in China. Statistical, content and lag sequence analysis methods were employed. Results showed that the three key roles had weak interaction persistence and different content quality in interactive participation characteristics. Knowledge construction behaviours of key roles were generally at a high level, with different patterns observed. Finally, the study proposed practical implications based on key roles' interactive participation characteristics and knowledge construction behaviour patterns. These implications may inspire the learning designs that promote online interaction and deep learning based on role characteristics in online teacher professional development.Practitioner notesWhat is already known about this topic Online teacher professional development is an evolving and increasingly recognized field within education, positioned as a subset of online learning. Within professional learning communities, teachers engage in knowledge construction and display varied role characteristics. In large‐scale online teacher professional development, only a few consistently maintained knowledge sharing and interaction. These active participants, or ‘key roles’, account for the majority of interactions. The interactive characteristics of the key roles are rarely discussed. What this paper adds Key roles, while actively participating in the online community, do not necessarily maintain consistent activity throughout the learning process. The interactive participation behaviour of key roles tends to learn more towards ‘following’ rather than ‘initiating’. The quality of interactive content from key roles varies widely. While they are responsible for a significant portion of interactions, it is not guaranteed that these interactions will always be of high quality. Knowledge construction behaviour patterns vary among key roles. Overall, their knowledge construction behaviours are generally at a high level and primarily transit among high stages of knowledge construction. Implications for practice Maintain continuous interaction and posting high‐quality content prove to be challenging even for key roles. Course administrators should monitor and strategically enhance their engagement to prevent them from becoming lurkers. Understanding the behaviour patterns of key roles offers insights for increasing interaction enthusiasm among active participants. It is necessary to develop role‐specific scripts in professional learning communities, to tailor content for opinion leaders, contributors and boundary spanners.

Mechanistic and evolutionary insights into isoform-specific ‘supercharging’ in DCLK family kinases

Catalytic signaling outputs of protein kinases are dynamically regulated by an array of structural mechanisms, including allosteric interactions mediated by intrinsically disordered segments flanking the conserved catalytic domain. The doublecortin-like kinases (DCLKs) are a family of microtubule-associated proteins characterized by a flexible C-terminal autoregulatory ‘tail’ segment that varies in length across the various human DCLK isoforms. However, the mechanism whereby these isoform-specific variations contribute to unique modes of autoregulation is not well understood. Here, we employ a combination of statistical sequence analysis, molecular dynamics simulations, and in vitro mutational analysis to define hallmarks of DCLK family evolutionary divergence, including analysis of splice variants within the DCLK1 sub-family, which arise through alternative codon usage and serve to ‘supercharge’ the inhibitory potential of the DCLK1 C-tail. We identify co-conserved motifs that readily distinguish DCLKs from all other calcium calmodulin kinases (CAMKs), and a ‘Swiss Army’ assembly of distinct motifs that tether the C-terminal tail to conserved ATP and substrate-binding regions of the catalytic domain to generate a scaffold for autoregulation through C-tail dynamics. Consistently, deletions and mutations that alter C-terminal tail length or interfere with co-conserved interactions within the catalytic domain alter intrinsic protein stability, nucleotide/inhibitor binding, and catalytic activity, suggesting isoform-specific regulation of activity through alternative splicing. Our studies provide a detailed framework for investigating kinome-wide regulation of catalytic output through cis-regulatory events mediated by intrinsically disordered segments, opening new avenues for the design of mechanistically divergent DCLK1 modulators, stabilizers, or degraders.

Mechanistic and evolutionary insights into isoform-specific 'supercharging' in DCLK family kinases.

Catalytic signaling outputs of protein kinases are dynamically regulated by an array of structural mechanisms, including allosteric interactions mediated by intrinsically disordered segments flanking the conserved catalytic domain. The doublecortin-like kinases (DCLKs) are a family of microtubule-associated proteins characterized by a flexible C-terminal autoregulatory 'tail' segment that varies in length across the various human DCLK isoforms. However, the mechanism whereby these isoform-specific variations contribute to unique modes of autoregulation is not well understood. Here, we employ a combination of statistical sequence analysis, molecular dynamics simulations, and in vitro mutational analysis to define hallmarks of DCLK family evolutionary divergence, including analysis of splice variants within the DCLK1 sub-family, which arise through alternative codon usage and serve to 'supercharge' the inhibitory potential of the DCLK1 C-tail. We identify co-conserved motifs that readily distinguish DCLKs from all other calcium calmodulin kinases (CAMKs), and a 'Swiss Army' assembly of distinct motifs that tether the C-terminal tail to conserved ATP and substrate-binding regions of the catalytic domain to generate a scaffold for autoregulation through C-tail dynamics. Consistently, deletions and mutations that alter C-terminal tail length or interfere with co-conserved interactions within the catalytic domain alter intrinsic protein stability, nucleotide/inhibitor binding, and catalytic activity, suggesting isoform-specific regulation of activity through alternative splicing. Our studies provide a detailed framework for investigating kinome-wide regulation of catalytic output through cis-regulatory events mediated by intrinsically disordered segments, opening new avenues for the design of mechanistically divergent DCLK1 modulators, stabilizers, or degraders.

Development, Identification and Validation of a Novel SSR Molecular Marker for Heat Resistance of Grapes Based on miRNA

The adverse effects of high temperatures on fruit quality and yield have been reported in many studies of grapevines. MiRNAs play crucial roles in plant growth and development and also fulfill functions in regulating the high-temperature response. In this research, miRNA-based SSR (simple sequence repeat) polymorphisms were analyzed according to the grape genome sequence and then combined with polymerase chain reaction (PCR) amplification and Sanger sequencing methods to analyze the miRNA-SSR diversity of different heat-resistant grape varieties. A statistical analysis of SSR sequences showed that 391 and 12 SSRs were specific to pri-miRNA and pre-miRNA, respectively. In pri-miRNA containing SSR, hexa-nucleotide repeats were the most abundant (52.69%), followed by tetra-nucleotide (13.04%) and minimum penta-nucleotide (4.09%), which were also observed in pre-miRNA sequences. On the other hand, differences in heat resistance among grape varieties were observed. Based on the results of leaf Fv/Fm images, values and phenotypic changes under high-temperature stress, 20 heat-resistant (e.g., Niagara Rosada and Grand Noir) and 20 heat-sensitive (e.g., Shine Muscat and Jumeigui) grape varieties were identified. Further, PCR-Sanger sequencing was used to screen SSR differences in four thermos-tolerant and four thermos-sensitive grape varieties, and finally, eight SSR differential primers were found to be able to distinguish these varieties. Of these, four pairs of primers were selected for validation in 40 grape germplasm resources (20 thermo-tolerance and 20 thermo-sensitivity). The VMIRSSR167c3 highlights that the ratio of SSR (CT) repeat values greater than 17 in heat-resistant varieties was 90%. In conclusion, the VMIRSSR167c3 marker can accurately distinguish between heat-resistant and heat-sensitive grape varieties. The results provide a novel molecular marker for the genetic improvement of grape germplasm resources and will be beneficial to the breeding of heat-resistant varieties in the future.

Transitions in daily search tactics: during the cross-app interaction search process

PurposeCross-app interactive search has become the new normal, but the characteristics of their tactic transitions are still unclear. This study investigated the transitions of daily search tactics during the cross-app interaction search process.Design/methodology/approachIn total, 204 young participants' impressive cross-app search experiences in real daily situations were collected. The search tactics and tactic transition sequences in their search process were obtained by open coding. Statistical analysis and sequence analysis were used to analyze the frequently applied tactics, the frequency and probability of tactic transitions and the tactic transition sequences representing characteristics of tactic transitions occurring at the beginning, middle and ending phases. FindingsCreating the search statement (Creat), evaluating search results (EvalR), evaluating an individual item (EvalI) and keeping a record (Rec) were the most frequently applied tactics. The frequency and probability of transitions differed significantly between different tactic types. “Creat? EvalR? EvalI? Rec” is the typical path; Initiate the search in various ways and modifying the search statement were highlighted at the beginning phase; iteratively creating the search statement is highlighted in the middle phase; Moreover, utilization and feedback of information are highlighted at the ending phase. Originality/valueThe present study shed new light on tactic transitions in the cross-app interactive environment to explore information search behaviour. The findings of this work provide targeted suggestions for optimizing APP query, browsing and monitoring systems.

Improving antibody thermostability based on statistical analysis of sequence and structural consensus data.

BackgroundThe use of Monoclonal Antibodies (MAbs) as therapeutics has been increasing over the past 30 years due to their high specificity and strong affinity toward the target. One of the major challenges toward their use as drugs is their low thermostability, which impacts both efficacy as well as manufacturing and delivery.MethodsTo aid the design of thermally more stable mutants, consensus sequence-based method has been widely used. These methods typically have a success rate of about 50% with maximum melting temperature increment ranging from 10 to 32°C. To improve the prediction performance, we have developed a new and fast MAbs specific method by adding a 3D structural layer to the consensus sequence method. This is done by analyzing the close-by residue pairs which are conserved in >800 MAbs’ 3D structures.ResultsCombining consensus sequence and structural residue pair covariance methods, we developed an in-house application for predicting human MAb thermostability to guide protein engineers to design stable molecules. Major advantage of this structural level assessment is in significantly reducing the false positives by almost half from the consensus sequence method alone. This application has shown success in designing MAb engineering panels in multiple biologics programs.ConclusionsOur data science-based method shows impacts in Mab engineering.

+microstate: A MATLAB toolbox for brain microstate analysis in sensor and cortical EEG/MEG

+microstate is a MATLAB toolbox for brain functional microstate analysis. It builds upon previous EEG microstate literature and toolboxes by including algorithms for source-space microstate analysis. +microstate includes codes for performing individual- and group-level brain microstate analysis in resting-state and task-based data including event-related potentials/fields. Functions are included to visualise and perform statistical analysis of microstate sequences, including novel advanced statistical approaches such as statistical testing for associated functional connectivity patterns, cluster-permutation topographic ANOVAs, and χ2 analysis of microstate probabilities in response to stimuli. Additionally, codes for simulating microstate sequences and their associated M/EEG data are included in the toolbox, which can be used to generate artificial data with ground truth microstates and to validate the methodology. +microstate integrates with widely used toolboxes for M/EEG processing including Fieldtrip, SPM, LORETA/sLORETA, EEGLAB, and Brainstorm to aid with accessibility, and includes wrappers for pre-existing toolboxes for brain-state estimation such as Hidden Markov modelling (HMM-MAR) and independent component analysis (FastICA) to aid with direct comparison with these techniques. In this paper, we first introduce +microstate before subsequently performing example analyses using open access datasets to demonstrate and validate the methodology. MATLAB live scripts for each of these analyses are included in +microstate, to act as a tutorial and to aid with reproduction of the results presented in this manuscript.

Affinity and Correlation in DNA

A statistical analysis of important DNA sequences and related proteins has been performed to study the relationships between monomers, and some general considerations about these macromolecules can be provided from the results. First, the most important relationship between sites in all the DNA sequences examined is that between two consecutive base pairs. This is an indication of an energetic stabilization due to the stacking interaction of these couples of base pairs. Secondly, the difference between human chromosome sequences and their coding parts is relevant both in the relationships between sites and in some specific compositional rules, such as the second Chargaff rule. Third, the evidence of the relationship in two successive triplets of DNA coding sequences generates a relationship between two successive amino acids in the proteins. This is obviously impossible if all the relationships between the sites are statistical evidence and do not involve causes; therefore, in this article, due to stacking interactions and this relationship in coding sequences, we will divide the concept of the relationship between sites into two concepts: affinity and correlation, the first with physical causes and the second without. Finally, from the statistical analyses carried out, it will emerge that the human genome is uniform, with the only significant exception being the Y chromosome.

METHOD OF STATISTICAL ANALYSIS OF RANDOM SEQUENCES GENERATED BY GENERATORS OF RANDOM AND PSEUDO-RANDOM NUMBER

The problems of the Internet of Things, in particular the need to protect confidential data, lead to the use of encryption and decryption methods, which, in turn, rely on the use of random numbers. The use of HVDC can help ensure the security of this process, but it is important to use high-quality HVDC and thoroughly test them to avoid security vulnerabilities. Statistical analysis is used to assess the randomness of the generated sequences by testing them using statistical tests of randomness. These tests evaluate various statistical properties, such as the frequency distribution of the generated numbers, the distribution of consecutive numbers, and the presence of patterns. By performing a statistical analysis, you can identify any flaws or biases in the operation of the random number generator and improve its quality. The article checks the sequence for randomness using two- and three-dimensional statistics. A mathematical model is built, a theorem is given. A scheme of statistical analysis of sequences was built, based on which software engineering methods were used. Examples of using binary statistics for bit sequence analysis are given. The use of statistical analysis of the randomness of sequences generated by random (or pseudo-random) number generators is a popular trend, as it is critical to the reliability and security of various computing systems and applications, including the Internet of Things (IoT), cryptography, simulations, and scientific calculation. With the growing importance of cybersecurity and data privacy, the demand for reliable and secure random number generators is increasing, and the use of statistical analysis to assess the randomness of sequences generated by random (or pseudo-random) number generators is becoming increasingly critical.

Coding triplets in the tRNA acceptor-TΨC arm and their role in present and past tRNA recognition.

The mechanism and evolution of the recognition scheme between key components of the translation system, that is, tRNAs, synthetases, and elongation factors, are fundamental issues in understanding the translation of genetic information into proteins. Statistical analysis of bacterial tRNA sequences reveals that for six amino acids, a string of 10 nucleotides preceding the tRNA 3' end carries cognate coding triplets to nearly full extent. The triplets conserved in positions 63-67 are implicated in the recognition by the elongation factor EF-Tu, and those conserved in positions 68-72, in the identification of cognate tRNAs, and their derived minihelices by class IIa synthetases. These coding triplets are suggested to have primordial origin, being engaged in aminoacylation of prebiotic tRNAs and in the establishment of the canonical codon set.

Анализ пространственного распределения сообществ макрозообентоса в равнинной реке полупустынной зоны

Анализ пространственного распределения сообществ макрозообентоса в равнинной реке полупустынной зоны

In silico analysis and molecular characterization of Influenza A (H1N1) pdm09 virus circulating and causing major outbreaks in central India, 2009-2019.

Background and Objectives:Influenza A/H1N1pdm09 causes respiratory illness and remains a concern for public health. Since its first emergence in 2009, the virus has been continuously circulating in the form of its genetic variants. Influenza A/H1N1pdm09 surveillance is essential for uncovering emerging variants of epidemiologic and vaccine efficacy. The present study attempts in silico analysis and molecular characterization of Influenza A (H1N1) pdm09 virus circulating and causing major outbreaks in central India during 2009–2019.Materials and Methods:We have investigated the antigenic drift analysis of 96 isolates’ hemagglutinin (HA) gene sequences (59 central Indian and 37 local Indian and 28 global reference HA gene sequences) of Influenza A/H1N1pdm09 viruses from 2009 to 2019. The study includes mutational (Multiple sequence Alignment), phylogenetic (Maximum Likelihood Method), and statistical analysis (Covariance and correlation) of HA sequences submitted in NCBI, IRD and GISAID from central India.Results:Phylogenetic analysis indicated maximum clustering of central Indian HA gene sequences in genogroup 6B. Analysis of amino acid sequence alignment revealed changes in receptor binding site (RBS). The frequency of S220T amino acid substitution was found to be high followed by S202T, K300E A273T, K180Q. The Karl Pearson correlation coefficient (r) and covariance between the number of mutations and the death toll was found +0.246 and +100.3 respectively.Conclusion:The study identifies the continuous genetic variations in the HA gene sequences of circulating Influenza A/H1N1pdm09 in central India from the year 2009 to 2019. Further suggesting importance of monitoring the gradual evolution of the virus with regards to an increase in virulence, pathogenicity and vaccine efficacy timely.

Detection of Pathogenic Bacteria Among Adult Ticks Collected in Conservation Areas and Private Lands of Adair County, Missouri

Three species of ticks have been found in Northeast Missouri, where the incidence and prevalence of tick-borne infections is among the highest in the United States of America. We collected ticks and detected the presence of Rickettsia montanensis (instead of R. rickettsii) and Ehrlichia chaffeensis and the absence of Anaplasma phagocytophilum infections among adult ticks collected in conservation and private areas of Adair County, Missouri. Pathogens were detected and identified using PCR, DNA sequencing of PCR fragments and statistical sequence analysis. We found Ehrlichia chaffeensis in 10% of ticks of the species Amblyomma amercanum and Rickettsia montanensis in 7.95% of collected ticks of the species Dermacentor variabilis. These findings stress the importance of continuous monitoring and study of vector populations in the region. Such monitoring can contribute to risk assessment of ehrlichiosis, spotted fever infections, and anaplasmosis, which are considered endemic in the region.

Hashing technique based on SHA-3 using chaotic maps1

New hashing technique based on SHA-3 (Secure Hash Algorithm-3) is introduced. Chaotic maps are used in this technique to enhance performance without losing security. Introduced algorithm was tested for resistance against collisions, statistical analysis of output sequences was performed, hashing performance was evaluated. The testing showed a low collision probability. The testing corresponds the standards of National Institute of Standards and Technology and showed that output sequences are close to random. Performance testing showed 60 % enhancement in comparison with plain SHA-3.