Sequence Analysis Methods Research Articles

INFLUENCE OF TIMESPANS ON CONSTRUCTION SEQUENCE ANALYSIS OF RC BUILDINGS

The sequence of construction, application of loads, and its timings are significant for the method of Construction Sequence Analysis (CSA). Any variation in these parameters significantly changes the responses of reinforced concrete (RC) structures, even without considering the effect of time-dependent properties of concrete like creep and shrinkage. When the effect of these factors is incorporated, the change in timespan has a significant impact on the overall responses of the structure. Many researchers have considered the response at the time of application of live load. This study indicates that it may lead to failure as it is less than the critical responses. The prevailing practice in India is to design RC buildings for 50 years of life span. In this study, responses like the axial shortening of columns and the bending moments at various sections of a selected beam of a 40 storeyed RC building are observed for the selected nine different timespans ranging from one to fifty years considering the effect of creep and shrinkage. These responses are compared with the response of the 50th year to arrive at a critical time span for analysis. The analysis is done on the MIDAS Gen-17 software. This study concludes that 98% to 99% of the 50th year responses are achieved within 30 years from the application of live load. The study recommends analyzing RC structures only up to the timespan of 30 years using the CSA method.

Application of Pulse Sequence Partial Discharge Based Convolutional Neural Network in Pattern Recognition for Underground Cable Joints

In this study, a pulse sequence analysis (PSA) method is presented, which does not measure the voltage signal for the defect-type recognition of power cable joints based on partial discharges (PDs) using convolutional neural networks (CNNs). The PSA showed effective results in comparison to a conventional phase-resolved PD (PRPD) pattern, which is a combination of a PD and voltage signals. The optimal drawing formats of the PSA patterns, including the size, type, color, and marker size of the images, which influence the recognition accuracy, were evaluated. In total, 12 sets of PD data, consisting of three types of cable joint defects, were trained and tested using CNN-based models. The results show that the performance of the PSA-based CNN model is as good as that of a PRPD even when the voltage signal is lost, whereas PRPD mostly fails under such cases. The total recognition accuracy obtained from a PSA-based CNN was 95.3%, whereas that of the PRPD-based CNN was 90.6%. In addition, an advanced and useful application of a PSA pattern is discussed herein.

장기요양 서비스 이용 패턴 유형 및 특성 연구

The purpose of this study is to categorize long-term care service use patterns and to identify differences and changes in characteristics by type. By applying the sequence analysis method to the Senior DB of the National Health Insurance Service(NHIS), the long-term care service use patterns were categorized. We tracked the transitions between long-term care in-home benefits, long-term care facilities (LTCFs), and long-term care hospitals (LTCHs) experienced by the same individual. Differences and changes in service use patterns type were comprehensively analyzed. As a result of the analysis, long-term care service use patterns were categorized into 11 types. The pattern type of continuing to use the service that has been started occupied the majority. The transition from home to facility(LTCFs/LTCHs) was subdivided into types by the transition period, while the transition from facility to home was not identified as a type. Among the types of transition from home to facility(LTCFs/LTCHs), the proportion of transition to LTCFs and transition to LTCHs was 2:1. While the transition from home to LTCFs accounts for a greater proportion, transitions from home to LTCHs have also been significant. As a result of comparing demographic characteristics, care needs, family care resources, and economic resources by type of long-term care service use pattern, a resource gap was found between the types. Accordingly, policy and practical tasks were proposed for rational use of long-term care services and support for living in the community.

Response and contribution of bacterial and archaeal communities to eutrophication in urban river sediments

Excessive loading of nitrogen (N) and phosphorus (P) that leads to eutrophication mutually interacts with sediment microbial community. To unravel the microbial community structures and interaction networks in the urban river sediments with the disturbance of N and P loadings, we used high-throughput sequencing analysis and ecological co-occurrence network methods to investigate the responses of diversity and community composition of bacteria and archaea and identify the keystone species in river sediments. The alpha-diversity of archaea significantly decreased with the increased total nitrogen (TN), whereas the operational taxonomic unit (OTU) number of bacteria increased with the increase of available phosphorus (AP). The beta-diversity of archaea and bacteria was more sensitive to N content than P content. The relative abundance of predominant bacterial and archaeal taxa varied differently in terms of different N and P contents. Complexity and connectivity of bacteria and archaea interaction networks showed significant variations with eutrophication, and competition between bacteria became more significant with the increase of N content. The sensitive and the highest connective species (keystone species) were identified for different N and P loadings. Total carbon (TC), water content (WC), microbial alpha-diversity and interaction networks played pivotal roles in the N and P transformation in urban river sediments.

Sequence Analysis and Feature Extraction of Sports Images Using Recurrent Neural Network

Image sequence analysis is attracting significant attention at present, but its principles and techniques have rarely been applied to the field of sports biomechanics. As far as the technology of automatic recognition of joint points by computers is concerned, it is still in the experimental stage. The purpose of this paper is to study and analyze the sequence analysis and feature extraction of sports images based on cyclic neural network. This paper puts forward the basic concepts of sports image sequence analysis and feature extraction and analyzes the importance of sports in this context. As the experimental results demonstrates, the application rate of detecting human motion by using template matching technology detection is between 15% and 47%, while the accuracy of image sequence analysis method has increased from 17% to about 65%. Generally speaking, although the template matching technology detection method is more popular than the image sequence analysis method at the beginning, the popularity of the image sequence analysis method is significantly higher than that of the template matching technology detection method after time precipitation. Therefore, it is very important to study the sequence analysis and feature extraction of sports image based on cyclic neural network.

Constructing benchmark test sets for biological sequence analysis using independent set algorithms.

Biological sequence families contain many sequences that are very similar to each other because they are related by evolution, so the strategy for splitting data into separate training and test sets is a nontrivial choice in benchmarking sequence analysis methods. A random split is insufficient because it will yield test sequences that are closely related or even identical to training sequences. Adapting ideas from independent set graph algorithms, we describe two new methods for splitting sequence data into dissimilar training and test sets. These algorithms input a sequence family and produce a split in which each test sequence is less than p% identical to any individual training sequence. These algorithms successfully split more families than a previous approach, enabling construction of more diverse benchmark datasets.

Internal migrant trajectories within The Netherlands, 1850–1972: Applying cluster analysis and dissimilarity tree methods

Based on the life course perspective, this work adopts a sequence analysis approach to examine internal migrant trajectories and their interdependencies with life course factors. The analyses are based on longitudinal data from the Historical Sample of the Netherlands. The internal migrant trajectories of Dutch cohorts born between 1850 and 1922 are followed from birth until age fifty. Two sequence analysis methods are applied: (1) main migration patterns are described using cluster analysis, and explained by their associations with socio-demographic covariates using logit models; (2) migrant trajectory variations are investigated using a dissimilarity tree method with a discrepancy analysis. Seven distinct migrant trajectory patterns are derived from the cluster analysis. Early-life social status, place and region of origin are differently associated to these typologies, and an increased stability of specific trajectories over time is suggested. Fifteen homogeneous migrant trajectories are identified in the dissimilarity tree. The discrepancy between groups is explained by intricate interactions between birth cohort, social and place origin, as well as family formation. The cluster analysis suggests a stable regime of internal migration patterns over time. The dissimilarity tree method contributes to detect interdependencies of migrant trajectories, highlighting socio-economic, local and regional differences at birth.

Assessing species coverage and assembly quality of rapidly accumulating sequenced genomes.

BackgroundAmbitious initiatives to coordinate genome sequencing of Earth's biodiversity mean that the accumulation of genomic data is growing rapidly. In addition to cataloguing biodiversity, these data provide the basis for understanding biological function and evolution. Accurate and complete genome assemblies offer a comprehensive and reliable foundation upon which to advance our understanding of organismal biology at genetic, species, and ecosystem levels. However, ever-changing sequencing technologies and analysis methods mean that available data are often heterogeneous in quality. To guide forthcoming genome generation efforts and promote efficient prioritization of resources, it is thus essential to define and monitor taxonomic coverage and quality of the data.FindingsHere we present an automated analysis workflow that surveys genome assemblies from the United States NCBI, assesses their completeness using the relevant BUSCO datasets, and collates the results into an interactively browsable resource. We apply our workflow to produce a community resource of available assemblies from the phylum Arthropoda, the Arthropoda Assembly Assessment Catalogue. Using this resource, we survey current taxonomic coverage and assembly quality at the NCBI, examine how key assembly metrics relate to gene content completeness, and compare results from using different BUSCO lineage datasets.ConclusionsThese results demonstrate how the workflow can be used to build a community resource that enables large-scale assessments to survey species coverage and data quality of available genome assemblies, and to guide prioritizations for ongoing and future sampling, sequencing, and genome generation initiatives.

Sequence Matching between Hemagglutinin and Neuraminidase through Sequence Analysis Using Machine Learning.

To date, many experiments have revealed that the functional balance between hemagglutinin (HA) and neuraminidase (NA) plays a crucial role in viral mobility, production, and transmission. However, whether and how HA and NA maintain balance at the sequence level needs further investigation. Here, we applied principal component analysis and hierarchical clustering analysis on thousands of HA and NA sequences of A/H1N1 and A/H3N2. We discovered significant coevolution between HA and NA at the sequence level, which is closely related to the type of host species and virus epidemic years. Furthermore, we propose a sequence-to-sequence transformer model (S2STM), which mainly consists of an encoder and a decoder that adopts a multi-head attention mechanism for establishing the mapping relationship between HA and NA sequences. The training results reveal that the S2STM can effectively realize the “translation” from HA to NA or vice versa, thereby building a relationship network between them. Our work combines unsupervised and supervised machine learning methods to identify the sequence matching between HA and NA, which will advance our understanding of IAVs’ evolution and also provide a novel idea for sequence analysis methods.

Metabarcoding of canopy arthropods reveals negative impacts of forestry insecticides on community structure across multiple taxa

Abstract Insecticides used to combat outbreaks of forest defoliators can adversely affect non‐target arthropods. Forestry insecticides typically suppress Lepidoptera larvae which are the cornerstone of the canopy community of deciduous oak forests. The abrupt removal of this dominant component of the food web could have far‐reaching implications for forest ecosystems, yet it is rarely investigated in practice owing to several methodological shortcomings. The taxonomic impediment and the biased nature of arthropod sampling techniques particularly impede the assessment of insecticide impacts on diverse communities. To tackle this issue, we propose an experimental approach combining sampling by pyrethrum knockdown and species determination via DNA metabarcoding, using community subsampling to derive estimates of species abundances. We applied this protocol to investigate the short‐term effects of the insecticides diflubenzuron (DFB) and Bacillus thuringiensis var. kurstaki (BTK) on canopy‐dwelling arthropod communities in German oak woodlands. Our approach allowed us to detect most of the diversity and integrate species abundances in our analyses. By classifying arthropod species into assemblages based on their expected sensitivity rather than coarse taxonomic groupings, we could unveil substantial effects of DFB across multiple taxa 5 weeks after application. Although strong effects on single species appear related to direct toxicity, substantial impacts of DFB on parasitoids and xylophagous beetles suggest that anti‐defoliator treatments can have previously unsuspected indirect effects on some components of forest arthropod communities. The impacts of BTK on community structure were consistent with that of DFB though much weaker. Synthesis and applications. Comparing diversity patterns in the arthropod communities of sprayed and unsprayed oak canopies, our results show that selective insecticides can alter species diversity in presumably non‐sensitive taxa. Even though the ecological significance of these impacts has yet to be assessed in an operational setting, their existence calls for increased regulatory scrutiny on indirect effects. As community approaches become more attainable with the rapid development of DNA metabarcoding, we suggest the inclusion of community‐level end points as regulatory requirements for the approval of forest‐use insecticides.

Investigation of Hepatitis C Virus (HCV) Genotypes by "Reverse Hybridisation Strip Assay" and DNA Sequence Analysis Methods

Hepatitis C Virus (HCV) is a virus that is estimated to infect approximately 185 million people worldwide and causes 350 000 deaths each year. The target in HCV treatment is the sustained virological response (SVR), and the most important virological factors determining SVR are genotype and baseline HCV-RNA levels. In this study, it was aimed to compare the HCV genotypes and subtypes detected by the "line probe assay (LIPA)" method with sequence analysis. HCV genotypes and subtypes were investigated by line probe assay (LIPA) (NLM analytica, Italy) and sequence analysis methods in 212 patients with chronic HCV diagnosis and with HCV RNA viral load of 10⁴ IU/ml and above. 5'UTR and core regions were studied in LIPA method and NS5B gene region was studied in the sequencing method. After amplifying 340 bp region in the NS5B gene region with the hemi-nested PCR method, sequence analysis (ABI 3500 Genetic Analyzer [Applied Biosystems, USA]) was performed. Statistical analysis of the study was determined by using TURCOSA Analytics program. In the study, HCV genotypes and subtypes that were determined in 212 patients by LIPA method were; 40 (18.8%) genotype 1a, 97 (45.75%) genotype 1b, 7 (3.3%) genotype 2, 12 (5.6%) genotype 2a/c, 2 (0.94%) genotype 2b, 19 (8.96%) genotype 3, 16 (7.55%) genotype 4, 1 (0.47%) genotype 4a, 15 (7.5%) genotype 4c/d, 1 (0.47%) genotype 4h and 2 (0.94%) genotype 5. The HCV genotypes and subtypes determined in 212 patients by sequence analysis were; 20 (9.43%) genotype 1a, 118 (55.6%) genotype 1b, 16 (7.55%) genotype 2a, 4 (1.89%) genotype 2b, 1 (0.47%) genotype 2k, 21 (9.91%) genotype 3a, 2 (0.94%) genotype 4a, 28 (13.21%) genotype 4d and 2 (0.94%) genotype 5a. The difference in results between the two methods was found to be statistically significant (p< 0.001). According to the viral load-genotype relationship, the highest viral load was detected in genotype 1a patients (p< 0.001). In conclusion, in order to determine HCV genotypes and subtypes in our study, LIPA and sequence analysis methods were compared and the genotype compatibility between the two methods were determined as 97.5% on the basis of genotype and 19% on the basis of subtype. Since the direct-acting antiviral (DEA) treatment protocol in chronic HCV patients is planned according to the genotype/subtype determination, the inconsistency of the results obtained in the LIPA method, which is routinely widely used, with the sequence analysis method is remarkable, and it was concluded that these results should be supported by studies containing more samples.

A Hierarchical Spatial-Temporal Embedding Method Based on Enhanced Trajectory Features for Ship Type Classification.

Ship type classification is an essential task in maritime navigation domains, contributing to shipping monitoring, analysis, and forecasting. Presently, with the development of ship positioning and monitoring systems, many ship trajectory acquisitions make it possible to classify ships according to their movement pattern. Existing methods of ship classification based on trajectory include classical sequence analysis and deep learning methods. However, the real ship trajectories are unevenly distributed in geographical space, which leads to many problems in inferring the ship movement mode on the original ship trajectory. This paper proposes a hierarchical spatial-temporal embedding method based on enhanced trajectory features for ship type classification. We first preprocess the trajectory and combine the port information to transform the original ship trajectory into the moored records of ships, removing the unevenly distributed points in the trajectory data and enhancing key points’ semantic information. Then, we propose a Hierarchical Spatial-Temporal Embedding Method (Hi-STEM) for ship classification. Hi-STEM maps moored records in the original geographical space into the feature space and can efficiently find the classification plane in the feature space. Experiments are conducted on real-world datasets and compared with several existing methods. The result shows that our approach has high accuracy in ship classification on ship moored records. We make the source code and datasets publicly available.

Alignment-free comparison of metagenomics sequences via approximate string matching.

Quantifying pairwise sequence similarities is a key step in metagenomics studies. Alignment-free methods provide a computationally efficient alternative to alignment-based methods for large-scale sequence analysis. Several neural network-based methods have recently been developed for this purpose. However, existing methods do not perform well on sequences of varying lengths and are sensitive to the presence of insertions and deletions. In this article, we describe the development of a new method, referred to as AsMac that addresses the aforementioned issues. We proposed a novel neural network structure for approximate string matching for the extraction of pertinent information from biological sequences and developed an efficient gradient computation algorithm for training the constructed neural network. We performed a large-scale benchmark study using real-world data that demonstrated the effectiveness and potential utility of the proposed method. The open-source software for the proposed method and trained neural-network models for some commonly used metagenomics marker genes were developed and are freely available at www.acsu.buffalo.edu/~yijunsun/lab/AsMac.html. Supplementary data are available at Bioinformatics online.

Geomorphic Evolution of Radial Sand Ridges in the South Yellow Sea Observed from Satellites

The radial sand ridges consist of more than 70 sand ridges that are spread out radially on the continental shelf of the South Yellow Sea. As a unique geomorphological feature in the world, its evolution process and characteristics are crucial to marine resource management and ecological protection. Based on the multi-source remote sensing image data from 1979 to 2019, three types of geomorphic feature lines, artificial coastlines, waterlines, and sand ridge lines were extracted. Using the GIS sequence analysis method (Digital Shoreline Analysis System (DSAS), spatial overlay analysis, standard deviational ellipse method), the evolution characteristics of the shoreline, exposed tidal flats, and underwater sand ridges from land to sea were interpreted. The results demonstrate that: (1) The coastline has been advancing towards the sea with a maximum advance rate of 348.76 m/a from Wanggang estuary to Xiaoyangkou Port. (2) The exposed tidal flats have decreased by 1484 km2 including the reclaimed area of 1414 km2 and showed a trend of erosion in the north around Xiyang channel and deposition in the southeast around the Gaoni and Jiangjiasha areas. (3) The overall sand ridge lines showed a trend of gradually moving southeast (135°), and the moving distance is nearly 4 km in the past 40 years. In particular, the sand ridge of Tiaozini has moved 11 km southward, while distances of 8 km for Liangyuesha and 5 km for Lengjiasha were also observed. For the first time, this study quantified the overall migration trend of the RSRs. The imbalance of the regional tidal wave system may be one of the main factors leading to the overall southeastward shift of the radiation sandbanks.

O AfCFTA no Contexto das Relações África-UE: Uma Análise Regulamentar de Alguns Fatos Estilizados

Abstract The inception of the African Continental Free Trade Area (AfCFTA) in 2018, an event which redefined the institutional dimensions of Africa-European relations, is at the centre of historic normative debates between two dominant contending schools on the imperatives of African unity. While Pan-Africanists advocate for African unity as a strategy to rupture trade relations between the two continents in question, Afro-European integrationists assert that the quest for African unity should not be divorced from deeper exchanges between Africa and Europe. The AfCFTA is embedded in an ensemble of Africa-EU free trade institutions and therefore is bound to be perceived by Pan-Africanists as Africa yielding to European domination and mobilized by Afro-European integrationists as a vindication of the validity of their assertions. With the aid of Bob Jessop’s regulation analysis, Radhika Desai’s concept of combined development, and my notion of syndic neo-corporatism as well as the method of sequence analysis, this paper makes a case beyond the normative perspectives of the two schools. It does so in order to demonstrate that while Pan-Africanists miss the point that African elites instrumentally value the AfCFTA as a rent extraction scheme, Afro-European integrationists fail to recognize that the AfCFTA fits the strategic quest of the EU to prevail over its global competitors.

A new feature vector model for alignment-free DNA sequence similarity analysis

Improvements in technology have triggered the production of big data. Within this scope, enormous amounts of biological data have been generated. A number of analysis methods have been developed to access the information contained in biological data. DNA sequence analysis has drawn particular attention in recent years. As an alternative to alignment-based sequence comparison methods that have high computational costs, alignment-free comparison methods have emerged. These m ethods c an c alculate sequence s imilarity by applying different dimensions of numerical characterizations. In this paper, we propose a novel alignment-free DNA sequence analysis method based on a feature extraction strategy. The method utilizes numerical characterization and is implemented by calculating mean distance of the transitions, mean distance of the nucleotide duplications, and the base frequencies. The method then measures the similarity between 7-dimensional vectors that are obtained through feature extraction. Using this approach, we conducted a sequence similarity analysis of two different DNA sequence datasets of different lengths to demonstrate the effectiveness of the method. The proposed method shows that a simple and successful feature vector can be obtained when DNA sequences having many properties are used in combination with appropriate and effective descriptors. With this strategy, reasonable results were obtained with a low computational cost.

MycoSNP: A Portable Workflow for Performing Whole-Genome Sequencing Analysis of Candida auris.

Candida auris is an urgent public health threat characterized by high drug-resistant rates and rapid spread in healthcare settings worldwide. As part of the C. auris response, molecular surveillance has helped public health officials track the global spread and investigate local outbreaks. Here, we describe whole-genome sequencing analysis methods used for routine C. auris molecular surveillance in the United States; methods include reference selection, reference preparation, quality assessment and control of sequencing reads, read alignment, and single-nucleotide polymorphism calling and filtration. We also describe the newly developed pipeline MycoSNP, a portable workflow for performing whole-genome sequencing analysis of fungal organisms including C. auris.

Interest Points Analysis for Internet Forum Based on Long-Short Windows Similarity

For Internet forum Points of Interest (PoI), existing analysis methods are usually lack of usability analysis under different conditions and ignore the long-term variation, which lead to blindness in method selection. To address this problem, this paper proposed a PoI variation prediction framework based on similarity analysis between long and short windows. Based on the framework, this paper presented 5 PoI analysis algorithms which can be categorized into 2 types, i.e., the traditional sequence analysis methods such as autoregressive integrated moving average model (ARIMA), support vector regressor (SVR), and the deep learning methods such as convolutional neural network (CNN), long-short term memory network (LSTM), Transformer (TRM). Specifically, this paper firstly divides observed data into long and short windows, and extracts key words as PoI of each window. Then, the PoI similarities between long and short windows are calculated for training and prediction. Finally, series of experiments is conducted based on real Internet forum datasets. The results show that, all the 5 algorithms could predict PoI variations well, which indicate effectiveness of the proposed framework. When the length of long window is small, traditional methods perform better, and SVR is the best. On the contrary, the deep learning methods show superiority, and LSTM performs best. The results could provide beneficial references for PoI variation analysis and prediction algorithms selection under different parameter configurations.

Molecular Diagnostics for Ocular Infectious Diseases: LXXVIII Edward Jackson Memorial Lecture

To review the use of molecular diagnostic techniques in the management of ocular infectious disease. Retrospective review. A combination of literature review and personal recollections are used. Although the broad term molecular diagnostics may encompass techniques to identify pathogens via protein or metabolomic signatures, this review concentrates on detection of pathogen nucleic acid as an indicator of infection. The introduction of the polymerase chain reaction (PCR) in 1985 opened a new era in analysis of nucleic acids. This technique was soon applied to the detection of potential pathogen DNA and RNA, including viruses, bacteria, and parasites in infectious eye disease. Advances in PCR have allowed class-specific diagnostics (ie, pan-bacterial and pan-fungal), quantitation of pathogen DNA, and multiplexed testing. The Human Genome Project in the early 2000s greatly accelerated development of DNA sequencers, ushering in the era of "Next Generation Sequencing" and permitting pathogen-agnostic methods for the detection of potential infectious agents. Most recently, new technologies such as nanopore sequencing have reduced both cost and equipment requirements for whole-genome sequencing; when coupled with real-time sequence analysis methods, these methods offer the promise of true, real-time, point-of-service ocular infectious disease diagnostics. Molecular methods for pathogen detection have greatly advanced the diagnosis of ocular infectious disease. Further methodologic advances will have a direct impact on the management of these conditions.

Scaling-up a fragment-based protein-protein interaction method using a human reference interaction set.

Protein–protein interactions (PPIs) are essential in understanding numerous aspects of protein function. Here, we significantly scaled and modified analyses of the recently developed all‐vs‐all sequencing (AVA‐Seq) approach using a gold‐standard human protein interaction set (hsPRS‐v2) containing 98 proteins. Binary interaction analyses recovered 20 of 47 (43%) binary PPIs from this positive reference set (PRS), comparing favorably with other methods. However, the increase of 20× in the interaction search space for AVA‐Seq analysis in this manuscript resulted in numerous changes to the method required for future use in genome‐wide interaction studies. We show that standard sequencing analysis methods must be modified to consider the possible recovery of thousands of positives among millions of tested interactions in a single sequencing run. The PRS data were used to optimize data scaling, auto‐activator removal, rank interaction features (such as orientation and unique fragment pairs), and statistical cutoffs. Using these modifications to the method, AVA‐Seq recovered >500 known and novel PPIs, including interactions between wild‐type fragments of tumor protein p53 and minichromosome maintenance complex proteins 2 and 5 (MCM2 and MCM5) that could be of interest in human disease.