High Complexity Research Articles

Handheld echocardiography for the screening and diagnosis of rheumatic heart disease: a systematic review to inform WHO guidelines

Early detection and diagnosis of acute rheumatic fever and rheumatic heart disease are key to preventing progression, and echocardiography has an important diagnostic role. Standard echocardiography might not be feasible in high-prevalence regions due to its high cost, complexity, and time requirement. Handheld echocardiography might be an easy-to-use, low-cost alternative, but its performance in screening for and diagnosing acute rheumatic fever and rheumatic heart disease needs further investigation. In this systematic review and meta-analysis, we searched Embase, MEDLINE, LILACS, and Conference Proceedings Citation Index-Science up to Feb 9, 2024, for studies on the screening and diagnosis of acute rheumatic fever and rheumatic heart disease using handheld echocardiography (index test) or standard echocardiography or auscultation (reference tests) in high-prevalence areas. We included all studies with useable data in which the diagnostic performance of the index test was assessed against a reference test. Data on test accuracy in diagnosing rheumatic heart disease, acute rheumatic fever, or carditis with acute rheumatic fever (primary outcomes) were extracted from published articles or calculated, with authors contacted as necessary. Quality of evidence was appraised using GRADE and QUADAS-2 criteria. We summarised diagnostic accuracy statistics (including sensitivity and specificity) and estimated 95% CIs using a bivariate random-effects model (or univariate random-effects models for analyses including three or fewer studies). Area under the curve (AUC) was calculated from summary receiver operating characteristic curves. Heterogeneity was assessed by visual inspection of plots. This study was registered with PROSPERO (CRD42022344081). Out of 4868 records we identified 11 studies, and two additional reports, comprising 15 578 unique participants. Pooled data showed that handheld echocardiography had high sensitivity (0·87 [95% CI 0·76-0·93]), specificity (0·98 [0·71-1·00]), and overall high accuracy (AUC 0·94 [0·84-1·00]) for diagnosing rheumatic heart disease when compared with standard echocardiography (two studies; moderate certainty of evidence), with better performance for diagnosing definite compared with borderline rheumatic heart disease. High sensitivity (0·79 [0·73-0·84]), specificity (0·85 [0·80-0·89]), and overall accuracy (AUC 0·90 [0·85-0·94]) for screening rheumatic heart disease was observed when pooling data of handheld echocardiography versus standard echocardiography (seven studies; high certainty of evidence). Most studies had a low risk of bias overall. Some heterogeneity was observed for sensitivity and specificity across studies, possibly driven by differences in the prevalence and severity of rheumatic heart disease, and level of training or expertise of non-expert operators. Handheld echocardiography has a high accuracy and diagnostic performance when compared with standard echocardiography for diagnosing and screening of rheumatic heart disease in high-prevalence areas. World Health Organization. For the Chinese, French, Italian, Persian, Portuguese, Spanish and Urdu translations of the abstract see Supplementary Materials section.

Uptake and outcomes of implementing non-invasive prenatal testing (Prenatal Reflex DNA testing) into first-trimester contingent screening protocols for trisomy 21, 18, 13: A study protocol

Chromosomal anomalies (CAs), such as trisomies 21 (T21), 18 (T18), and 13 (T13), are significant contributors to birth defects and genetic conditions worldwide. Cell-free fetal DNA testing (cff-DNA) is the preferred method for detecting these trisomies from maternal blood samples. However, its adoption is hindered by high costs and complexities. To enhance efficiency and reduce financial strain, we propose a practical protocol. This approach involves a contingent test based on biochemical and ultrasound markers commonly used in clinical practice. We aim to leverage cost-effective contingent prenatal DNA screening for trisomies 21, 18, and 13. This is integrated into standard aneuploidy screening during the first trimester at prenatal centers. Screening participants are categorized into four pathways (1T Quad, 1T Combined, 1T Expanded Combined, and 1T Expanded Combined + Ductus Venosus Pulsatility Index) based on biochemical markers (PAPP-A, Free β-hCG, PlGF, AFP) and ultrasound indicators (NT, DV-PI) as per FMF guidelines. Pregnant subjects (Low/High/Moderate Risk) with a high chance of affected pregnancy (1:1000 ≤ Risk < 1:10) undergo the DNA reflex screening test using stored plasma samples.

Lesion-symptom Mapping of Acceptability Judgments in Chronic Poststroke Aphasia Reveals the Neurobiological Underpinnings of Receptive Syntax.

Disagreements persist regarding the neural basis of syntactic processing, which has been linked both to inferior frontal and posterior temporal regions of the brain. One focal point of the debate concerns the role of inferior frontal areas in receptive syntactic ability, which is mostly assessed using sentence comprehension involving complex syntactic structures, a task that is potentially confounded with working memory. Syntactic acceptability judgments may provide a better measure of receptive syntax by reducing the need to use high working memory load and complex sentences and by enabling assessment of various types of syntactic violations. We therefore tested the perception of grammatical violations by people with poststroke aphasia (n = 25), along with matched controls (n = 16), using English sentences involving errors in word order, agreement, or subcategorization. Lesion data were also collected. Control participants performed near ceiling in accuracy with higher discriminability of agreement and subcategorization violations than word order; aphasia participants were less able to discriminate violations, but, on average, paralleled control participants discriminability of types of violations. Lesion-symptom mapping showed a correlation between discriminability and posterior temporal regions, but not inferior frontal regions. We argue that these results diverge from models holding that frontal areas are amodal core regions in syntactic structure building and favor models that posit a core hierarchical system in posterior temporal regions.

Free riding is not the problem: how agency, heterogeneity and authority challenge collective action against antimicrobial resistance in the European Union

ABSTRACT This paper outlines a contextual approach to the governance of collective action in high complexity contexts. Using the major public health threat of antimicrobial resistance (AMR) in Europe as empirical case, we investigate the coordination challenges related to a plurality of agency, heterogeneity of capabilities, preferences and institutions, and various degrees and forms of authority. The analysis draws on original quantitative and qualitative data on senior public servants’ and experts’ preferences for governance arrangements. Allowing for variation in agency, heterogeneity and authority, we identify factors preventing or facilitating transnational governance and explain how and why these factors persist. The pattern of experts’ governance preferences as indicated in surveys and interviews suggests that when collective action fails, it can often be attributed to the heterogeneity among EU member states in terms of their willingness and capacity to address AMR. Also, networks of AMR experts are seen as critical to successful governance, both in the human public health sector where formal authority is limited and in animal health where the EU has extensive formal authority. In human public health, networks are instruments for exchanging ideas, knowledge and learning while in animal health networks support hierarchies and facilitate horizontal coordination.

Effect of low versus high balance training complexity on balance performance in male adolescents

ObjectiveThe current study aimed to determine the effects of low (i.e., balance task only) versus high (i.e., balance task combined with an additional motor task like dribbling a basketball) balance training complexity (6 weeks of training consisting of 2 × 30 min balance exercises per week) on measures of static and dynamic balance in 44 healthy male adolescents (mean age: 13.3 ± 1.6 years).ResultsIrrespective of balance training complexity, significant medium- to large-sized pretest to posttest improvements were detected for static (i.e., One-Legged Stance test, stance time [s], 0.001 < p ≤ 0.008) and dynamic (i.e., 3-m Beam Walking Backward test, steps [n], 0.001 < p ≤ 0.002; Y-Balance-Test-Lower-Quarter, reach distance [cm], 0.001 < p ≤ 0.003) balance performance. Further, in all but one comparison (i.e., stance time with eyes opened on foam ground) no group × test interactions were found. These results imply that balance training is effective to improve static and dynamic measures of balance in healthy male adolescents, but the effectiveness seems unaffected by the applied level of balance training complexity.

Novel Aluminum Alloy Tailored for Additive Manufacturing: Structural Characterization and Qualification Perspectives

The recent advances achieved in additive manufacturing (AM) technology demonstrate the potential to realize customized metal components, ensuring weight reduction opportunities. These benefits make AM attractive for high-cost aerospace applications, especially where high geometric complexity is required. In the context of an EU research scenario, the H2020 MANUELA (Additive Manufacturing Using Metal Pilot Line) project promotes the development of new technologies for design optimization by enabling the application of novel materials in AM. This paper illustrates recent advances in a new aluminum alloy (Al-HS1) with high strength emphasizing all of the characterization steps at the coupon level. This material has been employed in the re-engineering of a conventional hydraulic manifold using a powder bed fusion-laser beam (PBF-LB) process. Both the simulations and structural tests allowed for proving its compliance and technological maturity with industrial standards and applicable airworthiness requirements.

Simultaneous Optimization of Launch Vehicle Stage and Trajectory Considering Flight-Requirement Constraints

AbstractA conceptual design of a launch vehicle encompasses various disciplines, such as structural design, aerodynamics, propulsion systems, flight control, and stage sizing. This paper focuses on optimizing trajectory and stage size. Traditional approaches used for the conceptual design of a launch vehicle conduct the stage and trajectory designs sequentially, often leading to high computational complexity and suboptimal results. This paper presents an optimization framework that addresses both trajectory optimization and staging in an integrated way. The proposed framework aims to minimize the lift-off mass while satisfying the constraints on flight requirements (e.g., maximum dynamic pressure and the impact points of separated parts). A case study demonstrates the advantage of the proposed framework compared to the traditional sequential optimization approach.

Permutation entropy and complexity analysis of large-scale solar wind structures and streams

Abstract. In this work, we perform a statistical study of magnetic field fluctuations in the solar wind at 1 au using permutation entropy and complexity analysis and the investigation of the temporal variations of the Hurst exponents. Slow and fast wind, magnetic clouds, interplanetary coronal mass ejection (ICME)-driven sheath regions, and slow–fast stream interaction regions (SIRs) have been investigated separately. Our key finding is that there are significant differences in permutation entropy and complexity values between the solar wind types at larger timescales and little difference at small timescales. Differences become more distinct with increasing timescales, suggesting that smaller-scale turbulent features are more universal. At larger timescales, the analysis method can be used to identify localised spatial structures. We found that, except in magnetic clouds, fluctuations are largely anti-persistent and that the Hurst exponents, in particular in compressive structures (sheaths and SIRs), exhibit a clear locality. Our results shows that, in all cases apart from magnetic clouds at the largest scales, solar wind fluctuations are stochastic, with the fast wind having the highest entropies and low complexities. Magnetic clouds, in turn, exhibit the lowest entropy and highest complexity, consistent with them being coherent structures in which the magnetic field components vary in an ordered manner. SIRs, slow wind and ICME sheaths are intermediate in relation to magnetic clouds and fast wind, reflecting the increasingly ordered structure. Our results also indicate that permutation entropy–complexity analysis is a useful tool for characterising the solar wind and investigating the nature of its fluctuations.

Exploration of the Reform of Multi subject Co construction Data Structure Experiment Course under the Mixed Teaching Mode

In response to the high complexity of algorithm courses and the difficulty for students to understand, this paper focuses on the data structure experimental course and proposes a multi subject co construction data structure experimental course reform strategy under the blended teaching mode. The article first explores the problems in the content and methods of practical courses, and then proposes reform measures in teaching modes, teaching systems, and teaching methods. At the same time, on the basis of school enterprise cooperation, design differentiated experimental projects, teach students according to their aptitude, and strive for a student-centered teaching model, collaborative assistance to complete the role exchange between teaching and learning.

Streamlining IoT-driven Data Using Blockchain

The integration of Internet of Things (IoT) technology has revolutionized data collection and analytics, creating vast networks of interconnected devices generating large volumes of data. However, this rapid expansion of IoT ecosystems introduces significant challenges related to data security, integrity, scalability, and privacy. Blockchain technology, with its decentralized and immutable nature, emerges as a compelling solution to address these challenges. This paper explores the synergy between IoT and blockchain, examining how blockchain can streamline IoT-driven data by providing secure data exchanges, enhancing transparency, and facilitating scalability. The study investigates the key mechanisms through which blockchain can mitigate common IoT data issues. It considers blockchain's role in ensuring data integrity by utilizing cryptographic methods, enabling traceability through distributed ledgers, and protecting against unauthorized data manipulation. Furthermore, it explores the potential of blockchain-based smart contracts to automate processes and improve interoperability among IoT devices. The paper delves into specific use cases, such as smart cities, demonstrating how blockchain can effectively streamline IoT data across various industries. The findings highlight that while blockchain offers substantial benefits, it also introduces unique challenges, including high energy consumption, latency concerns, and complex integration processes. This concludes with a discussion of Security concerns, Comparison with existing solutions. By addressing these challenges, blockchain can become a pivotal technology in streamlining IoT-driven data, paving the way for more secure, efficient, and trustworthy IoT ecosystems.

Symmetry-Breaking of Nanoparticle Surface Function Via Conformal DNA Design.

Asymmetric surface functionalization of complex nanoparticles to control their directional self-assembly remains a considerable challenge. Here, we demonstrated a conformal DNA design strategy for flexible remodeling of the surface of complex nanoparticles, taking Au nanobipyramids (AuNBPs) as a model. We sheathed one or both tips of AuNBPs into conformal DNA origami with an exceptionally accurate orientation control. Such asymmetrically and symmetrically distributed surface patches possess regioselective, sequence, and site-specific DNA binding capabilities. As a result, we realized a series of prototypical multicomponent "colloidal molecules" made of AuNBPs and Au nanospheres (AuNSs) with defined directionality and number of "bonding valence" as well as 1D and 3D hierarchical assemblies, e.g., inverse core-satellites of AuNBPs and AuNSs, side-by-side and tip-to-tip linear assemblies of AuNBPs, and 3D helical superstructures of AuNBPs with tunable twists. These findings inspire new opportunities for nanoparticle surface engineering and the high-order self-assembly of nanoarchitectures with higher complexity and broadened functionalities.

Assessing the Suitability of Automation Using the Methods–Time–Measurement Basic System

Due to its high complexity and the varied assembly processes, hybrid assembly systems characterized by human–robot collaboration (HRC) are meaningful. Suitable use cases must be identified efficiently to ensure cost-effectiveness and successful deployment in the respective assembly systems. This paper presents a method for evaluating the potential of HRC to derive automation suitability based on existing or to-be-collected time data. This should enable a quick and favorable statement to be made about processes, for efficient application in potential analyses. The method is based on the Methods–Time–Measurement Basic System (MTM-1) procedure, widely used in the industry. This ensures good adaptability in an industrial context. It extends existing models and examines how much assembly activities and processes can be optimized by efficiently allocating between humans and robots. In the process model, the assembly processes are subdivided and analyzed with the help of the specified MTM motion time system. The suitability of the individual activities and sub-processes for automation are evaluated based on criteria derived from existing methods. Two four-field matrices were used to interpret and classify the analysis results. The process is assessed using an example product from electrolyzer production, which is currently mainly assembled by hand. To achieve high statement reliability, further work is required to classify the results comprehensively.

Research on Improved Road Visual Navigation Recognition Method Based on DeepLabV3+ in Pitaya Orchard

Traditional DeepLabV3+ image semantic segmentation methods face challenges in pitaya orchard environments characterized by multiple interference factors, complex image backgrounds, high computational complexity, and extensive memory consumption. This paper introduces an improved visual navigation path recognition method for pitaya orchards. Initially, DeepLabV3+ utilizes a lightweight MobileNetV2 as its primary feature extraction backbone, which is augmented with a Pyramid Split Attention (PSA) module placed after the Atrous Spatial Pyramid Pooling (ASPP) module. This improvement enhances the spatial feature representation of feature maps, thereby sharpening the segmentation boundaries. Additionally, an Efficient Channel Attention Network (ECANet) mechanism is integrated with the lower-level features of MobileNetV2 to reduce computational complexity and refine the clarity of target boundaries. The paper also designs a navigation path extraction algorithm, which fits the road mask regions segmented by the model to achieve precise navigation path recognition. Experimental findings show that the enhanced DeepLabV3+ model achieved a Mean Intersection over Union (MIoU) and average pixel accuracy of 95.79% and 97.81%, respectively. These figures represent increases of 0.59 and 0.41 percentage points when contrasted with the original model. Furthermore, the model’s memory consumption is reduced by 85.64%, 84.70%, and 85.06% when contrasted with the Pyramid Scene Parsing Network (PSPNet), U-Net, and Fully Convolutional Network (FCN) models, respectively. This reduction makes the proposed model more efficient while maintaining high segmentation accuracy, thus supporting enhanced operational efficiency in practical applications. The test results for navigation path recognition accuracy reveal that the angle error between the navigation centerline extracted using the least squares method and the manually fitted centerline is less than 5°. Additionally, the average deviation between the road centerlines extracted under three different lighting conditions and the actual road centerline is only 2.66 pixels, with an average image recognition time of 0.10 s. This performance suggests that the study can provide an effective reference for visual navigation in smart agriculture.

Long short-term memory (LSTM) neural networks for predicting dynamic responses and application in piezoelectric energy harvesting

Abstract Long Short-Time Memory (LSTM) deep neural networks are capable of learning order dependence in sequence problems and capturing long-term, non-linear temporal dependencies between the input and out of a system. With the long-term vision to model dynamical systems to which analytical or numerical methods are impossible or difficult to apply, this paper presents a study of modeling system dynamics and predicting responses using the LSTM networks, which have demonstrated excellent capability in predicting single-mode responses in a prior study. However, the LSTM network exhibits difficulties in modeling and predicting multi-mode responses accurately. To resolve the multi-mode issue, this paper presents an approach that obtains an equivalent network consisting of a set of sub-networks learned on isolated modes, and demonstrates its effectiveness on a simulated 2-degree-of-freedom (2DOF) mass-spring-damper system of nonlinear Duffing springs. The second part of the paper is focused on the application of the proposed approach in piezoelectric energy harvesting. Experiments are conducted on a harvester subjected to random base-motion excitation and exhibiting nonlinearity in its multi-mode response. Both the direct and mode-separation LSTM modeling approaches are applied to predict the output voltage given a random base-motion excitation. The mode-separation approach outperforms the direct approach significantly, and yields an excellent match between the actual and predicted responses. Specifically, for a test electrical voltage response of RMS value 0.2241 V, the difference between the actual test and predicted responses by using the mode-separation approach has an RMS value of 0.0504 V, compared to 0.1645 V obtained by using the direct LSTM approach. It is also much lower than the RMS value of 0.1835 V obtained by using the attention-based LSTM network, another comparison method. Leveraging a deep learning strategy, the validated approach opens up opportunities for accurately modeling energy harvesting systems of high complexities and/or strong nonlinearities.

Anticipating Trends and Innovations for A Sustainable Automotive Industrial Revolution

Graph classification aims to predict the label associated with a graph and is an important graph analytic task with widespread applications. Recently, graph neural networks (GNNs) have achieved state-of-the-art results on purely supervised graph classification by virtue of the powerful representation ability of neural networks. However, almost all of them ignore the fact that graph classification usually lacks reasonably sufficient labelled data in practical scenarios due to the inherent labelling difficulty caused by the high complexity of graph data. The existing semi-supervised GNNs typically focus on the task of node classification and are incapable of dealing with graph classification. To tackle the challenging but practically useful scenario, we propose a novel and general semi-supervised GNN framework for graph classification, which takes full advantage of a slight amount of labelled graphs and abundant unlabelled graph data. In our framework, we train two GNNs as complementary views for collaboratively learning high-quality classifiers using both labelled and unlabelled graphs. To further exploit the view itself, we constantly select pseudo-labelled graph examples with high confidence from its own view for enlarging the labelled graph dataset and enhancing predictions on graphs. Furthermore, the proposed framework is investigated on two specific implementation regimes with a few labelled graphs and the extremely few labelled graphs, respectively. Extensive experimental results demonstrate the effectiveness of our proposed semi-supervised GNN framework for graph classification on several benchmark datasets

Suppressed conductive loss of flower-like NiCo2O4-wrapped flaky FeSiAl for excellent low-frequency microwave absorption

Flaky FeSiAl is a high potential absorbent for low-frequency microwave applications. However, impedance mismatching due to its excessively high complex permittivity presents a challenge. Herein, inspired by the surface modification strategy, flaky FeSiAl wrapped with flower-like NiCo2O4 (NiCo2O4@flaky FeSiAl) was developed via a hydrothermal method. The introduction of a surface NiCo2O4 insulating layer was expected to reduce the complex permittivity and increase the high complex permeability in the NiCo2O4@flaky FeSiAl composite. Subsequently, an excellent low-frequency microwave absorption performance was achieved. The designed NiCo2O4@flaky FeSiAl displayed a robust minimum refection loss (RL min) of −33.41 dB at 6.32 GHz and increased the effective absorption bandwidth (EAB, RL < −10 dB) to 4.08 GHz when the matching thickness was 1.6 mm. The enhanced low-frequency microwave absorption performance was mainly attributed to the improvement in impedance matching, which originated from the collaborative regulation of electromagnetic parameters. This study provides a promising approach for designing flaky FeSiAl-based composites and high-performance low-frequency microwave absorbents.

A novel sparse recovery space‐time adaptive processing algorithm using the log‐sum penalty to approximate the ℓ0 − norm penalty

AbstractApplying the sparse recovery (SR) technique to airborne radar space‐time adaptive processing (STAP) can greatly reduce the number of required training samples, which is advantageous in detecting targets in non‐homogeneous and non‐stationary clutter environments. However, the poor performance, the slow convergence speed or the high computational complexity of the traditional SR STAP algorithms limit their practical application. To tackle this problem, a novel efficient SR STAP algorithm is proposed. The newly proposed SR STAP algorithm utilises the log‐sum penalty to approximate the penalty, which exhibits improved convergence performance and clutter suppression performance compared to the traditional SR STAP algorithms. Besides, the proposed algorithm can ensure the convergence in each iteration by offering a closed‐form analytic solution. Furthermore, the result of the mathematical derivation demonstrates the essential equivalence between our method and the iterative reweighted method. By utilising this equivalence, two additional methods are proposed that incorporate the knowledge of the clutter Capon spectrum and the clutter spectrum of the iterative adaptive approach (IAA) as the components of weighted values, resulting in further performance improvement of the proposed algorithm. Finally, simulation results with both simulated data and Mountain‐Top data demonstrate the high effectiveness and performance of the proposed methods.

Carbon Dioxide Capture under Low-Pressure Low-Temperature Conditions Using Shaped Recycled Fly Ash Particles

Carbon-capture technologies are extremely abundant, yet they have not been applied extensively worldwide due to their high cost and technological complexities. This research studies the ability of polymerized fly ash to capture carbon dioxide (CO2) under low-pressure and low-temperature conditions via physical adsorption. The research also studies the ability to desorb CO2 due to the high demand for CO2 in different industries. The adsorption–desorption hysteresis was measured using infrared-sensor detection apparatus. The impact of the CO2 injection rate for adsorption, helium injection rate for desorption, temperature, and fly ash contact surface area on the adsorption–desorption hysteresis was investigated. The results showed that change in the CO2 injection rate had little impact on the variation in the adsorption capacity; for all CO2 rate experiments, the adsorption reached more than 90% of the total available adsorption sites. Increasing the temperature caused the polymerized fly ash to expand, thus increasing the available adsorption sites, thus increasing the overall adsorption volume. At low helium rates, desorption was extremely lengthy which resulted in a delayed hysteresis response. This is not favorable since it has a negative impact on the adsorption–desorption cyclic rate. Based on the results, the polymerized fly ash proved to have a high CO2 capture capability and thus can be applied for carbon-capture applications.

Subsystem complexity and measurements in holography

We investigate the impact of measuring one subsystem on the holographic complexity of another. While a naive expectation might suggest a reduction in complexity due to the collapse of the state to a trivial product state during quantum measurements, our findings reveal a counterintuitive result: in numerous scenarios, measurements on one subsystem can amplify the complexity of another. We first present a counting argument elucidating this complexity transition in random states. Then, employing the subregion “complexity=volume” (CV) proposal, we identify a complexity phase transition induced by projective measurements in various holographic CFT setups, including CFT vacuum states, thermofield double states, and the joint system of a black hole coupled to a bath. According to the AdS/BCFT correspondence, the post-measurement dual geometry involves an end-of-the-world brane created by the projective measurement. The complexity phase transition corresponds to the transition of the entanglement wedge to the one connected to the brane. In the context of the thermofield double setup, complete projection on one side can transform the other side into a boundary state black hole with higher complexity or a pure AdS with lower complexity. In the joint system of a black hole coupled to a nongraviting bath, where (a part of) the radiation is measured, the BCFT features two boundaries: one for the black hole and the other for the measurement. We construct the bulk dual involving intersecting or non-intersecting branes, and investigate the complexity transition induced by the projective measurement. Notably, for a subsystem that contains the black hole brane, its RT surface may undergo a transition, giving rise to a complexity jump.

Isotope-Edited Variable Temperature Infrared Spectroscopy for Measuring Transition Temperatures of Single A-T Watson-Crick Base Pairs in DNA Duplexes.

Experimental methods to determine transition temperatures for individual base pair melting events in DNA duplexes are lacking despite intense interest in these thermodynamic parameters. Here, we determine the dimensions of the thymine (T) C2═O stretching vibration when it is within the DNA duplex via isotopic substitutions at other atomic positions in the structure. First, we determined that this stretching state was localized enough to specific atoms in the molecule to make submolecular scale measurements of local structure and stability in high molecular weight complexes. Next, we develop a new isotope-edited variable temperature infrared method to measure melting transitions at various locations in a DNA structure. As an initial test of this "sub-molecular scale thermometer", we applied our T13C2 difference infrared signal to measure location-dependent melting temperatures (TmL) in a DNA duplex via variable temperature attenuated total reflectance Fourier transform infrared (VT-ATR-FTIR) spectroscopy. We report that the TmL of a single Watson-Crick A-T base pair near the end of an A-T rich sequence (poly T) is ∼34.9 ± 0.7°C. This is slightly lower than the TmL of a single base pair near the middle position of the poly T sequence (TmL ∼35.6±0.2°C). In addition, we also report that the TmL of a single Watson-Crick A-T base pair near the end of a 50% G-C sequence (12-mer) is ∼52.5 ± 0.3°C, which is slightly lower than the global melting Tm of the 12-mer sequence (TmL ∼54.0±0.9°C). Our results provide direct physical evidence for end fraying in DNA sequences with our novel spectroscopic methods.