Sequence Of Segments Research Articles

Future movement plans interact in sequential arm movements.

Real-world actions often comprise a series of movements that cannot be entirely planned before initiation. When these actions are executed rapidly, the planning of multiple future movements needs to occur simultaneously with the ongoing action. How the brain solves this task remains unknown. Here, we address this question with a new sequential arm reaching paradigm that manipulates how many future reaches are available for planning while controlling execution of the ongoing reach. We show that participants plan at least two future reaches simultaneously with an ongoing reach. Further, the planning processes of the two future reaches are not independent of one another. Evidence that the planning processes interact is twofold. First, correcting for a visual perturbation of the ongoing reach target is slower when more future reaches are planned. Second, the curvature of the current reach is modified based on the next reach only when their planning processes temporally overlap. These interactions between future planning processes may enable smooth production of sequential actions by linking individual segments of a long sequence at the level of motor planning.

Future movement plans interact in sequential arm movements

Real-world actions often comprise a series of movements that cannot be entirely planned before initiation. When these actions are executed rapidly, the planning of multiple future movements needs to occur simultaneously with the ongoing action. How the brain solves this task remains unknown. Here, we address this question with a new sequential arm reaching paradigm that manipulates how many future reaches are available for planning while controlling execution of the ongoing reach. We show that participants plan at least two future reaches simultaneously with an ongoing reach. Further, the planning processes of the two future reaches are not independent of one another. Evidence that the planning processes interact is twofold. First, correcting for a visual perturbation of the ongoing reach target is slower when more future reaches are planned. Second, the curvature of the current reach is modified based on the next reach only when their planning processes temporally overlap. These interactions between future planning processes may enable smooth production of sequential actions by linking individual segments of a long sequence at the level of motor planning.

Assessment of Antifungal Potential of Rhizospheric Bacterial Isolates of Daucus carota against Rhizoctonia solani

Rhizoctonia solani is an important plant pathogen which is responsible for causing a number of plant diseases in a variety of cropping plants. The current studies were carried out to explore antifungal potential of bacteria isolated from rhizospheric region of Daucus carota against Rhizoctonia. Of the total 21 bacterial isolates isolated, only 3 isolates namely CVO-3, CVO-4 and CRY-O-2 were shown antifungal potential against Rhizoctonia. Besides having antifungal potential these isolates also have some plant growth promoting attributes that will be an extra benefit of using these isolates as a biofungicides against R. solani. Thus, these isolates could be used as biofungicides against Rhizoctonia to prevent a variety of plant diseases like sheath blight in Oryza sativa, stem canker and black scurf in Solanum tuberosum and seedling diseases in Brassica napus. The isolates CVO-3, CVO-4 and CRY-O-2 were also characterized biochemical level by using methods based on Determinative Bacteriology given in Bergey’s manual and at molecular level using 16S rRNA sequencing and sequences of 16S rDNA segment were submitted to NCBI in GenBank database with the accession numbers OR775574 (Lysinibacillus boronitolerans ss3cvo3), OR775575 (Lysinibacillus boronitolerans ss4cvo4) and OR708523 (Shouchella rhizosphaerae ss1cryo2). The present investigation is an approach towards the use of these bacterial isolates from carrot rhizosphere to control the activities in the rhizosphere of other cropping plants.

Arctium lappa is a new Natural Host of Tomato Spotted Wilt Virus in Korea.

Burdock (Arctium lappa L., belongs to the family Asteraceae), is an edible plant and an oriental medicinal herb in Korea (Han and Koo, 1993). In July 2023, burdocks showing chlorotic ringspots and yellowing on the leaves were observed in nine of approximately 4,000 plants in a greenhouse in Daegu, South Korea. To determine the causal virus species, nine symptomatic leaves from each individual plant were collected and tested using commercially available immunostrips (Agdia, Elkhart, USA) for cucumber mosaic virus (CMV) and tomato spotted wilt virus (TSWV). Seven out of nine samples tested positive for TSWV only. TSWV in South Korea was first reported on sweet pepper from Yesan in 2004 (Kim et al., 2004) and has since spread to various crops. The first report of TSWV infecting burdock plants in the world was from Hawaii in 1995 (Bautista et al., 1995), but TSWV-infected burdock has not been reported in Korea. To further confirm the presence of TSWV, total RNA was extracted from TSWV-positive burdock leaves using the RNeasy Plant Mini Kit (Qiagen, Hilden, Germany) and used in reverse transcription-polymerase chain reaction (RT-PCR) assays with a specific primer set that amplifies 777 bp of nucleocapsid gene (N gene) of TSWV (Yoon et al., 2014). To obtain the complete genome sequence of this TSWV in the burdock plant, named TSWV-DG, fragments of L, M, and S segments were amplified and sequenced. The complete genome sequences of the L (8914 nt), M (4773 nt), and S (2946 nt) segments were obtained by overlapping RT-PCR amplicons. RT-PCR products were cloned into the pGEM-T Easy vector, and selected DNA clones were sequenced using Sanger method (Bioneer, Korea). The complete genome sequences were deposited to GenBank (LC790665, LC790666, and LC790667, respectively). BLASTn analysis showed that sequences of each TSWV-DG segment had maximum nucleotide identities of 99.5%, 99.5%, and 99.5% with TSWV-L, TSWV-M, and TSWV-S (OM154971, OM154970, and OM154969, respectively), which were isolated from water dropwort (Oenanthe crocata) in China (Qiu et al., 2023). To assess the biological activity of TSWV-DG, A. lappa and Nicotiana benthamiana were inoculated mechanically with sap from infected burdock leaves and maintained for visual inspection of virus symptoms at 25 ℃ at 3 weeks. TSWV-DG produced symptoms on the systemic leaves of A. lappa, that included chlorotic spots and yellowing, and on the leaves of N. benthamiana, that included chlorotic spots and mosaic patterns from 14 days-post-inoculation. Meanwhile, mock-inoculated A.lappa and N.benthamiana remained symptomless. The presence of TSWV on the inoculated leaves was subsequently confirmed through Immunostrip and RT-PCR analyses. TSWV may pose a significant threat to the production of A. lappa, which is cultivated as both leafy greens and root vegetables in Korea. Furthermore, A. lappa may not only be at risk of damage from TSWV infection but also act as a potential source of TSWV infection, thereby posing a risk of transmission to other key crops in Korea, such as pepper or potato (Yoon et al., 2014). This is the first report TSWV infecting burdock in South Korea.

Virtual Array-Based Signal Detection and Carrier Frequency Offset Estimation in a Multistatic Collaborative Passive Detection System

To tackle the problem of difficult signal detection and carrier frequency synchronization faced by wireless communication among stations of the multistatic passive detection system in interference environments, an adaptive signal detection and carrier frequency offset (CFO) estimation method based on a virtual array is proposed in this paper. This is a data-aided method that utilizes a training sequence composed of three segments of sub-training sequences with different symbols. This method first uses spatial spectrum estimation to obtain the coarse frequency estimations of interference signals and CFO from virtual array signals constructed from the first two sub-training sequences. Then, beamforming is conducted accordingly on the virtual array signals constructed from the third sub-training sequence to suppress the in-band interferences and protrude the expected signal. Finally, improved performance of signal detection and CFO estimation is obtained with the beamformed signals. Simulation experiments show that a missed detection probability as low as 1 × 10−4, with a false detection probability of 1 × 10−3, can be obtained under a signal-to-interference ratio (SIR) of −10 dB and Eb/N0 of 1 dB. Moreover, the proposed method can also simultaneously achieve a CFO estimation error that is lower than 3%, with the condition of Eb/N0 being as low as −5 dB under different SIRs. Simulation results validate the proposed method and demonstrate the promising application prospects of the proposed method in networked passive detection scenarios.

PULMO: Precise utterance-level modeling for speech anti-spoofing

In recent years, most state-of-the-art approaches for spoofed speech detection have been based on convolutional neural networks (CNNs). Most neural networks, including CNNs, are trained in minibatch units, where all input data in each minibatch must have the same shape. Therefore, for minibatch training, each utterance is first either padded or truncated because utterances are variable-length sequences and thus cannot be directly fed into networks in minibatch units. However, modeling either a padded or truncated utterance, rather than the original one, makes it unfeasible to capture the entire context as is: padding could propagate even unwanted information, like artifacts, in the original utterance, and truncation inevitably loses some information. With these information distortions, model could get stuck in a suboptimal solution. To fill this gap, we proposeÚ a method for precise utterance-level modeling that enables minibatch-wise utterance-level modeling of variable-length utterances while minimizing the information distortions. The proposed method comprises sequence segmentation followed by segment aggregation. Sequence segmentation feeds variable-length utterances in the minibatch unit by decomposing each of them into fixed-length segments, which enables parallel processing of variable-length utterances without the uncertainty in input length. Segment aggregation plays a role in aggregating the segment embeddings by utterance to encode the entire information of each utterance. The experimental results of the evaluation trials of ASVspoof 2019 and 2021 indicate that the proposed method shows up to 84.9 % and 97.6 % relative equal error rate reductions on logical and physical access scenarios, respectively. Furthermore, the proposed method reduced the FLOPs for an epoch by 6 %.

Classification of Brain Magnetic Resonance Imaging Abnormalities and Spectrum of Neurological Findings in a Cohort with Copy Number Variation-Related Disorders

Introduction: Copy number variation (CNV) is the difference in the sequence of genomic segments, which can vary from one kilobase to several megabases. Certain CNVs have been linked to various human disorders, such as intellectual disability, multiple congenital anomalies, autism spectrum disorders, neurodegenerative and neuropsychiatric conditions, and cancer. The present study aims to classify brain magnetic resonance imaging (MRI) findings, describe neurological manifestations, and discuss the findings within the context of genotype–phenotype correlations in a cohort of patients with recurrent and nonrecurrent CNVs. Methods: A total of 21 patients with pathogenic CNV detected using microarray analysis were included in the study. Results: Analysis of the clinical findings of the patient cohort showed that 16 (76%) had microcephaly, 14 had epilepsy (66%), 20 had facial dysmorphism (95%), and all had developmental delay (100%). Novel brain MRI findings were detected in six (6/13, 46%) patients with recurrent CNV and five (5/8, 63%) patients with nonrecurrent CNV. Conclusion: CNV-related disorders should be considered in the differential diagnosis of patients with brain MRI findings suspicious for metabolic disorders. Brain MRI differences in patients with the same chromosomal deletion can be explained by the second-hit hypothesis. Additional single nucleotide variations and epigenetic factors in these cases may have led to the involvement of different regions of the brain. Revealing the phenotypic and genotypic characteristics of cases in rare disorders will contribute to the widespread use of precision medicine and genetic treatment approaches in the near future.

Accuracy of full arch scans using the iTero Element 2® intra-oral scanner: A clinical study.

To investigate the accuracy of full arch scans taken using the iTero Element 2® under clinical settings. Prospective clinical study. A customised upper removable appliance (URA) with four spheres (A-D) was fabricated from a maxillary arch iTero® scan for a 24-year-old patient. Six linear values were measured on the URA using a high-accuracy coordinate measuring machine. A total of 60 intra-oral iTero® scans were taken by two operators (HA and AS) with the test URA inserted to the participant's maxillary dentition at T1 and T2. Geomagic Control X software was used to measure the six linear distances between the reference spheres (1-4) in all scans. Comparisons between distances on full arch scans to true distances on the URA were made to evaluate accuracy. The median distance to the prepared bench top URA mean was significantly away from zero for each operator and timepoint across all measurements except AD versus 14 and BD versus 24. Statistically significant, although clinically non-significant, differences in median linear distances in relation to the prepared benchtop URA were observed between evaluators in segments AB versus 12 (operator 1 -0.021 mm vs. operator 2 -0.06 mm) and AC vs. 13 (operator 1 -0.044 mm vs. operator 2 -0.167 mm) at T1. The accuracy of scanners is defined as the trueness and precision of the captured scans to the dental arches. The accuracy of iTero Element 2® is acceptable for diagnosis and treatment planning in orthodontics. Length and location of the scanned segment and scanning sequence 'protocol' affect accuracy.

Secure discovery of genetic relatives across large-scale and distributed genomic data sets.

Finding relatives within a study cohort is a necessary step in many genomic studies. However, when the cohort is distributed across multiple entities subject to data-sharing restrictions, performing this step often becomes infeasible. Developing a privacy-preserving solution for this task is challenging owing to the burden of estimating kinship between all the pairs of individuals across data sets. We introduce SF-Relate, a practical and secure federated algorithm for identifying genetic relatives across data silos. SF-Relate vastly reduces the number of individual pairs to compare while maintaining accurate detection through a novel locality-sensitive hashing (LSH) approach. We assign individuals who are likely to be related together into buckets and then test relationships only between individuals in matching buckets across parties. To this end, we construct an effective hash function that captures identity-by-descent (IBD) segments in genetic sequences, which, along with a new bucketing strategy, enable accurate and practical private relative detection. To guarantee privacy, we introduce an efficient algorithm based on multiparty homomorphic encryption (MHE) to allow data holders to cooperatively compute the relatedness coefficients between individuals and to further classify their degrees of relatedness, all without sharing any private data. We demonstrate the accuracy and practical runtimes of SF-Relate on the UK Biobank and All of Us data sets. On a data set of 200,000 individuals split between two parties, SF-Relate detects 97% of third-degree or closer relatives within 15 h of runtime. Our work enables secure identification of relatives across large-scale genomic data sets.

A DNA-peptide probe coupled mass spectrometry-based method for the detection of LncRNA in hepatocyte and serum samples

Liver cancer, a prominent contributor to cancer-related deaths, necessitates timely identification for successful intervention. In recent years, long non-coding RNAs (lncRNAs) have gained attention as potential biomarkers for liver cancer, and accurate quantification of lncRNA is essential for early diagnosis of liver cancer. However, existing methods for lncRNA detection rely on amplification and labeling techniques, resulting in semi-quantitative or qualitative outcomes. DNA-peptide technology offers a promising alternative. It has successfully translated nucleic acid quantification into peptide quantification, particularly in the context of miRNA analysis. However, the intricate structure of long-chain lncRNAs presents a challenge not encountered with miRNAs, making the design and acquisition of specific DNA-peptide probes for direct quantification more difficult. In this study, a strategy for lncRNA quantification was developed using a DNA-peptide probe coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based quasi-targeted proteomics. The inherent structural complexity of long-chain lncRNAs was overcome by designing DNA-peptide probes based on their unique sequence segments, enabling direct quantification without the need for amplification. Highly upregulated in liver cancer (HULC) was identified as a target lncRNA through bioinformatics analysis using GEO2R online software. Subsequently, the target lncRNA (i.e., HULC), which had been biotinylated and bound to streptavidin agarose beforehand, was hybridized with the probe. Following trypsin digestion, the reporter peptide was released and quantified using LC-MS/MS. Finally, this method was applied to analyze HULC levels in liver cancer cell lines and serum samples, enabling quantitative assessment and early diagnosis. The results demonstrated that liver cancer diagnostics could be enhanced through the integration of mass spectrometry and DNA-peptide probe technology for lncRNA quantification.

Peer Assessment to improve the Pronunciation of Sibilant Sequences in English at the University Level

Phonetics plays a crucial role in the instruction of future English language teachers. Even more so when it comes to identifying and correcting pronunciation errors through Peer Assessment. The objective of this research was to identify segmental mispronunciation sequences among Ecuadorian university students. Using Demirezen's Auditory Articulation Model, the research focused on discovering pronunciation errors. The study was designed as action research with a quasi-experimental approach, incorporating a pre/post-test, a pronunciation validation sheet and a structured interview. The results reveal a significant improvement in phonetic performance. In addition, word intelligibility was affected by the incorrect concretization of the sibilant phonemes in English, for which a correction phase was intervened. Finally, the effectiveness of peer assessment in improving the pronunciation and speaking skills of English teachers was reaffirmed.

Constructing networks for comparison of collagen types.

Collagens are structural proteins that are predominantly found in the extracellular matrix of multicellular animals, where they are mainly responsible for the stability and structural integrity of various tissues. All collagens contain polypeptide strands (α-chains). There are several types of collagens, some of which differ significantly in form, function, and tissue specificity. Because of their importance in clinical research, they are grouped into subdivisions, the so-called collagen families, and their sequences are often analysed. However, problems arise with highly homologous sequence segments. To increase the accuracy of collagen classification and prediction of their functions, the structure of these collagens and their expression in different tissues could result in a better focus on sequence segments of interest. Here, we analyse collagen families with different levels of conservation. As a result, clusters with high interconnectivity can be found, such as the fibrillar collagens, the COL4 network-forming collagens, and the COL9 FACITs. Furthermore, a large cluster between network-forming, FACIT, and COL28a1 α-chains is formed with COL6a3 as a major hub node. The formation of clusters also signifies, why it is important to always analyse the α-chains and why structural changes can have a wide range of effects on the body.

Improving Deep Learning-Based Algorithm for Ploidy Status Prediction Through Combined U-NET Blastocyst Segmentation and Sequential Time-Lapse Blastocysts Images.

Several approaches have been proposed to optimize the construction of an artificial intelligence-based model for assessing ploidy status. These encompass the investigation of algorithms, refining image segmentation techniques, and discerning essential patterns throughout embryonic development. The purpose of the current study was to evaluate the effectiveness of using U-NET architecture for embryo segmentation and time-lapse embryo image sequence extraction, three and ten hr before biopsy to improve model accuracy for prediction of embryonic ploidy status. A total of 1.020 time-lapse videos of blastocysts with known ploidy status were used to construct a convolutional neural network (CNN)-based model for ploidy detection. Sequential images of each blastocyst were extracted from the time-lapse videos over a period of three and ten hr prior to the biopsy, generating 31.642 and 99.324 blastocyst images, respectively. U-NET architecture was applied for blastocyst image segmentation before its implementation in CNN-based model development. The accuracy of ploidy prediction model without applying the U-NET segmented sequential embryo images was 0.59 and 0.63 over a period of three and ten hr before biopsy, respectively. Improved model accuracy of 0.61 and 0.66 was achieved, respectively with the implementation of U-NET architecture for embryo segmentation on the current model. Extracting blastocyst images over a 10 hr period yields higher accuracy compared to a three-hr extraction period prior to biopsy. Combined implementation of U-NET architecture for blastocyst image segmentation and the sequential compilation of ten hr of time-lapse blastocyst images could yield a CNN-based model with improved accuracy in predicting ploidy status.

First record of Garra kempi Hora, 1921 (Cypriniformes: Cyprinidae) from Lohandra River of Nepal

Nepal holds a high ichthyofaunal diversity, nevertheless, the molecular study of Nepalese fish is still in its early stages. The first record of a cyprinid fish species the Garra kempi Hora, 1921 for Nepal was reported from eastern Nepal’s Lohandra River. Both morphology and molecular data affirmed the existence of G. kempi in Nepal’s aquatic system. This species was previously reported from China (Tibet) and northern India. The maximum likelihood phylogenetic analysis and pairwise genetic distance based on Kimura 2 parameters using cytochrome oxidase subunit 1 (COI) segment sequences (665 bp) also confirmed the identity of the species as G. kempi. The COI sequences of specimens from Nepal formed a monophyletic clade with the sequence of G. kempi from northeastern India and the two contained a pairwise genetic distance of 1.8% only. The new record of G. kempi from Nepal warrants a detailed ichthyofaunal survey for documenting the fish diversity in Nepal.

Features and quality metrics datasets for video coding in DASH

Nowadays, the majority of media Internet traffic consists of H.264-encoded streaming videos due to its high compatibility. One of the most popular streaming technology used to deliver videos over Internet is Dynamic Adaptive Streaming over HTTP (DASH). It transmit the video as a sequence of independent short video segments tailored to the receiver’s limitations (related to several factors such as available bandwidth or resolution on reception), aiming to enhance the Quality of Experience. In this paper, we present two types of datasets created from 4065 video segments of 2 seconds. The first type consists of extracting features related to color, space, and time from the segments across different vertical resolutions (240, 360, 480, 720, 1080, 1440 and 4K). The second one includes several quality metrics obtained from the same segments when they are encoded with different compression levels in the different resolutions.

Monitoring of industrial crystallization processes through image sequence segmentation and characterization

Monitoring of industrial crystallization processes through image sequence segmentation and characterization

Empowering Communication: A Deep Learning Framework for Arabic Sign Language Recognition with an Attention Mechanism

This article emphasises the urgent need for appropriate communication tools for communities of people who are deaf or hard-of-hearing, with a specific emphasis on Arabic Sign Language (ArSL). In this study, we use long short-term memory (LSTM) models in conjunction with MediaPipe to reduce the barriers to effective communication and social integration for deaf communities. The model design incorporates LSTM units and an attention mechanism to handle the input sequences of extracted keypoints from recorded gestures. The attention layer selectively directs its focus toward relevant segments of the input sequence, whereas the LSTM layer handles temporal relationships and encodes the sequential data. A comprehensive dataset comprised of fifty frequently used words and numbers in ArSL was collected for developing the recognition model. This dataset comprises many instances of gestures recorded by five volunteers. The results of the experiment support the effectiveness of the proposed approach, as the model achieved accuracies of more than 85% (individual volunteers) and 83% (combined data). The high level of precision emphasises the potential of artificial intelligence-powered translation software to improve effective communication for people with hearing impairments and to enable them to interact with the larger community more easily.

Genetic Reassortment in a Child Coinfected with Two Influenza B Viruses, B/Yamagata Lineage and B/Victoria-Lineage Strains.

We identified a child coinfected with influenza B viruses of B/Yamagata and B/Victoria lineages, in whom we analyzed the occurrence of genetic reassortment. Plaque purification was performed using a throat swab specimen from a 9-year-old child, resulting in 34 well-isolated plaques. The genomic composition of eight gene segments (HA, NA, PB1, PB2, PA, NP, M, and NS genes) for each plaque was determined at the lineage level. Of the 34 plaques, 21 (61.8%) had B/Phuket/3073/2013 (B/Yamagata)-like sequences in all gene segments, while the other 13 (38.2%) were reassortants with B/Texas/02/2013 (B/Victoria)-like sequences in 1-5 of the 8 segments. The PB1 segment had the most B/Victoria lineage genes (23.5%; 8 of 34 plaques), while PB2 and PA had the least (2.9%; 1 of 34 plaques). Reassortants with B/Victoria lineage genes in 2-5 segments showed the same level of growth as viruses with B/Yamagata lineage genes in all segments. However, reassortants with B/Victoria lineage genes only in the NA, PB1, NP, or NS segments exhibited reduced or undetectable growth. We demonstrated that various gene reassortments occurred in a child. These results suggest that simultaneous outbreaks of two influenza B virus lineages increase genetic diversity and could promote the emergence of new epidemic strains.

DIAS: A dataset and benchmark for intracranial artery segmentation in DSA sequences

The automated segmentation of Intracranial Arteries (IA) in Digital Subtraction Angiography (DSA) plays a crucial role in the quantification of vascular morphology, significantly contributing to computer-assisted stroke research and clinical practice. Current research primarily focuses on the segmentation of single-frame DSA using proprietary datasets. However, these methods face challenges due to the inherent limitation of single-frame DSA, which only partially displays vascular contrast, thereby hindering accurate vascular structure representation. In this work, we introduce DIAS, a dataset specifically developed for IA segmentation in DSA sequences. We establish a comprehensive benchmark for evaluating DIAS, covering full, weak, and semi-supervised segmentation methods. Specifically, we propose the vessel sequence segmentation network, in which the sequence feature extraction module effectively captures spatiotemporal representations of intravascular contrast, achieving intracranial artery segmentation in 2D+Time DSA sequences. For weakly-supervised IA segmentation, we propose a novel scribble learning-based image segmentation framework, which, under the guidance of scribble labels, employs cross pseudo-supervision and consistency regularization to improve the performance of the segmentation network. Furthermore, we introduce the random patch-based self-training framework, aimed at alleviating the performance constraints encountered in IA segmentation due to the limited availability of annotated DSA data. Our extensive experiments on the DIAS dataset demonstrate the effectiveness of these methods as potential baselines for future research and clinical applications. The dataset and code are publicly available at https://doi.org/10.5281/zenodo.11401368 and https://github.com/lseventeen/DIAS.

The Role of (Re)Syllabification on Coarticulatory Nasalization: Aerodynamic Evidence from Spanish

Tautosyllabic segment sequences exhibit greater gestural overlap than heterosyllabic ones. In Spanish, it is presumed that word-final consonants followed by a word-initial vowel undergo resyllabification, and generative phonology assumes that canonical CV.CV# and derived CV.C#V onsets are structurally identical. However, recent studies have not found evidence of this structural similarity in the acoustics. The current goal is to investigate anticipatory and carryover vowel nasalization patterns in tautosyllabic, heterosyllabic, and resyllabified segment sequences in Spanish. Nine native speakers of Peninsular Spanish participated in a read-aloud task. Nasal airflow data were extracted using pressure transducers connected to a vented mask. Each participant produced forty target tokens with CV.CV# (control), CVN# (tautosyllabic), CV.NV# (heterosyllabic), and CV.N#V (resyllabification) structures. Forty timepoints were obtained from each vowel to observe airflow dynamics, resulting in a total of 25,200 datapoints analyzed. Regarding anticipatory vowel nasalization, the CVN# sequence shows an earlier onset of nasalization, while CV.NV# and CV.N#V sequences illustrate parallel patterns among them. Carryover vowel nasalization exhibited greater nasal spreading than anticipatory nasalization, and vowels in CV.NV# and CV.N#V structures showed symmetrical nasalization patterns. These results imply that syllable structure affects nasal gestural overlap and that aerodynamic characteristics of vowels are unaffected across word boundaries.