Data Structure Research Articles

Sparse multi-view image clustering with complete similarity information

Multi-view image clustering aims to efficiently divide the collection of images by studying the characteristics of different views. Many studies performed Laplacian dimensionality reduction on the original image to avoid noise interference in constructing the similarity matrix. However, they ignored the problem that local similarity information and isolated image information are lost due to dimensionality reduction. These problems will result in the similarity matrix being insufficient to accurately describe the similarity between images, which in turn affects the clustering accuracy. Therefore, we propose a sparse multi-view spectral clustering model with complete similarity information between images (SMSC-CSI). The model combines the original image space and the low-dimensional spectral embedding space based on the adaptive neighbors method to learn the initial similarity matrices of each view jointly. On the one hand, the original space can retain all the similarity information between images so that the initial similarity matrix can accurately describe the similarity between images, which is conducive to accurate clustering. On the other hand, the low-dimensional space can avoid noise interference and retain the main structure of high-dimensional data, improving the robustness of the initial similarity matrix. Meanwhile, to ensure consistency among the views, the model minimizes the difference between the initial similarity matrix and the central fusion matrix by alternately updating to obtain the optimal weights and central fusion matrix for each view. Finally, we can obtain ideal clustering results directly with low model complexity and without post-processing steps such as K-means by adding non-negative Laplace rank constraints and ℓ0-norm constraints to the model objective function. Experimental results on different real image datasets show that SMSC-CSI can outperform some traditional clustering models and recent models on multi-view image clustering.

Structure Suture Learning-Based Robust Multiview Palmprint Recognition.

Low-quality palmprint images will degrade the recognition performance, when they are captured under the open, unconstraint, and low-illumination conditions. Moreover, the traditional single-view palmprint representation methods have been difficult to express the characteristics of each palm strongly, where the palmprint characteristics become weak. To tackle these issues, in this article, we propose a structure suture learning-based robust multiview palmprint recognition method (SSL_RMPR), which comprehensively presents the salient palmprint features from multiple views. Unlike the existing multiview palmprint representation methods, SSL_RMPR introduces a structure suture learning strategy to produce an elastic nearest neighbor graph (ENNG) on the reconstruction errors that simultaneously exploit the label information and the latent consensus structure of the multiview data, such that the discriminant palmprint representation can be adaptively enhanced. Meanwhile, a low-rank reconstruction term integrating with the projection matrix learning is proposed, in such a manner that the robustness of the projection matrix can be improved. Particularly, since no extra structure capture term is imposed into the proposed model, the complexity of the model can be greatly reduced. Experimental results have proven the superiority of the proposed SSL_RMPR by achieving the best recognition performances on a number of real-world palmprint databases.

Conservation genomics assessment of Tharp's bluestar (Amsonia tharpii) with comparisons to widespread (A. longilora) and narrowly endemic (A. fugatei) congeners.

Land-use change and habitat fragmentation are threats to biodiversity. The decrease in available habitat, increase in isolation, and mating within populations can lead to elevated inbreeding, lower genetic diversity, and poor fitness. Here we investigate the genetics of two rare and threatened plant species, Amsonia tharpii and A. fugatei, and we compare them to a widespread congener A. longiflora. We also report the first phylogenetic study of the genus Amsonia (Apocynaceae), including 10 of the 17 taxa and multiple sampling locations, to understand species relationships. We used a double digest restriction-site associated DNA sequencing (ddRADseq) approach to investigate the genetic diversity and gene flow of each species and to create a maximum likelihood phylogeny. The ddRADseq data was mapped to a reference genome to separate out the chloroplast and nuclear markers for population genetic analysis. Our results show that genetic diversity and inbreeding were low across all three species. The chloroplast and nuclear dataset in A. tharpii were highly structured, whereas they showed no structure for A. fugatei, while A. longiflora lacked structure for nuclear data but not chloroplast. Phylogenetic results revealed that A. tharpii is distinct and sister to A. fugatei, and together they are distantly related to A. longiflora. Our results demonstrated that evolutionary history and contemporary ecological processes largely influences genetic diversity within Amsonia. Interestingly, we show that in A. tharpii there was significant structure despite being pollinated by large, bodied hawkmoths that are known to be able to carry pollen long distances, suggesting that other factors are contributing to the structure observed among A. tharpii populations. Conservation efforts should focus on protecting all of the A. tharpii populations, as they contain unique genetic diversity, and a protection plan for A. fugatei needs to be established due to its limited distribution.

K-dStHash tree for indexing big spatio-temporal datasets

Today’s era is witness of tremendous ever growing spatial, temporal and spatiotemporal data. The huge spatio-temporal data immensely pushes the need for design and development of novel methods tailored for indexing spatio-temporal data. In this research paper, we propose the design of a novel spatio-temporal data indexing method, named as k-dStHash. We have proposed the algorithm k-dStHashInsertion for inserting spatio-temporal objects and an algorithm k-dStHashSrchPlaceTime has been used to search for the objects at given location and time. It is able to handle datasets with duplicate keys which has been ignored in many research works. Though the algorithm k-dStHashInsertion takes 1.3-1.5 times longer time to insert data in k-dStHash data structure as it needs to find a specific location to organize data efficiently, but when it comes to search for required records it is even more than 90 times faster when analyzed in comparison to brute force method. It is generalized enough to organize any kind of k-dimensional data and time-based data also including object finding, fleet management, clustering, leader identification, nearest neighbor, human/animal tracking, path finding and many more.

An oblique transpressive soft-collision records the amalgamation of the Siderian-Orosirian Mineiro belt (Brazil) and the São Francisco paleo-craton (Kenorland Supercontinent): insights from the Bom Sucesso metasedimentary sequence

The southernmost São Francisco craton carries a long and polycyclic tectonic evolution from the Archean to the Proterozoic. The worldwide characterizations of rocks formed between these Eons, such as those of the Bom Sucesso metasedimentary sequence, is key to understand significant changes on Earth’s geodynamic and atmosphere. This sequence is located between two important geotectonic domains in Brazil: the Archean São Francisco paleo-craton (a piece of the Kenorland Supercontinent) and the Siderian-Orosirian Mineiro belt. Although the Bom Sucesso metasedimentary sequence presents a strategic position to investigate the docking between different tectonic domains, longtime tectono-metamorphic episodes challenge the full understanding of the broad tectonic scenario. This work was performed aiming to unravel the links between the tectonic evolution of the Bom Sucesso metasedimentary sequence and their adjoining domains, represented by the Archean São Francisco paleo-craton and the Paleoproterozoic Mineiro belt. The original sedimentation of the Lagoa da Prata unit at the base of the Bom Sucesso metasedimentary sequence, the Bom Sucesso Banded Iron Formation and the Tabuãozinho unit developed in a passive margin setting with different Archean sources associated with nearby orthogneisses and metagranites of the São Francisco paleo-craton. This sequence yields an MDA of 2615 ± 4 Ma and is chronocorrelated to the Moeda Fm. at the base of the Minas Supergroup. After the deposition of the Bom Sucesso metasedimentary sequence in a passive margin setting, the amalgamation of the Paleoproterozoic Mineiro belt to the São Francisco paleo-craton between ca. 2.07 and 2.00 Ga by a soft-collision process produced two distinct compressional events. The first with N-S direction (σ1 = 181/16) originated two kilometer-scale thrust faults, associated with a transcurrent movement between the Bom Sucesso metasedimentary sequence and both the adjoining Archean and Paleoproterozoic domains. The same compressional event also developed tectonic foliations under amphibolite facies conditions (2.8 to 6.3 kbar; 550 to 632°C) and imprints a sillimanite-andalusite isograd along the Ibituruna and Bom Sucesso ridges. The second event has a NW-SE direction (σ1=333/18) and produced crosscutting structures under a ductile-brittle regime at lower temperatures been considered as retrogressive. A thermal influence of the Brasiliano orogeny in the region is suggested by new LA-ICPMS zircon Concordia age of 623 ± 4 Ma obtained in a garnet schist of the Lagoa da Prata unit, which is in agreement with Neoproterozoic Concordia Lower intercepts (probably Pb-loss) reported in zircon grains, and Ar-Ar ages (biotite) obtained along most of the Mineiro Belt area. This age may imply in a superposition of temporally distinct metamorphic events (Paleoproterozoic and Neoproterozoic) on the rocks of the Bom Sucesso metasedimentary sequence. The structural, metamorphic, and geochronologic data presented here brings new insights on structural and P-T-X conditions of the Paleoproterozoic geotectonic evolution of the southernmost São Francisco craton.

Posttraumatic survivor guilt is associated with white matter microstructure alterations

BackgroundMilitary veterans with posttraumatic stress disorder (PTSD) commonly experience posttraumatic guilt. Guilt over commission or omission evolves when responsibility is assumed for an unfortunate outcome (e.g., the death of a fellow combatant). Survivor guilt is a state of intense emotional distress experienced by the weight of knowing that one survived while others did not. MethodsThis study of the Translational Research Center for TBI and Stress Disorders (TRACTS) analyzed structural and diffusion-weighted magnetic resonance imaging data from 132 male Iraq/Afghanistan veterans with PTSD. The Clinician-Administered PTSD Scale for DSM-IV (CAPS-IV) was employed to classify guilt. Thirty (22.7 %) veterans experienced guilt over acts of commission or omission, 34 (25.8 %) experienced survivor guilt, and 68 (51.5 %) had no posttraumatic guilt. White matter microstructure (fractional anisotropy, FA), cortical thickness, and cortical volume were compared between veterans with guilt over acts of commission or omission, veterans with survivor guilt, and veterans without guilt. ResultsVeterans with survivor guilt had significantly lower white matter FA compared to veterans who did not experience guilt (p < .001), affecting several regions of major white matter fiber bundles. There were no significant differences in white matter FA, cortical thickness, or volumes between veterans with guilt over acts of commission or omission and veterans without guilt (p > .050). LimitationsThis cross-sectional study with exclusively male veterans precludes inferences of causality between the studied variables and generalizability to the larger veteran population that includes women. ConclusionSurvivor guilt may be a particularly impactful form of posttraumatic guilt that requires specific treatment efforts targeting brain health.

Adversarial Learning Based Node-Edge Graph Attention Networks for Autism Spectrum Disorder Identification.

Graph neural networks (GNNs) have received increasing interest in the medical imaging field given their powerful graph embedding ability to characterize the non-Euclidean structure of brain networks based on magnetic resonance imaging (MRI) data. However, previous studies are largely node-centralized and ignore edge features for graph classification tasks, resulting in moderate performance of graph classification accuracy. Moreover, the generalizability of GNN model is still far from satisfactory in brain disorder [e.g., autism spectrum disorder (ASD)] identification due to considerable individual differences in symptoms among patients as well as data heterogeneity among different sites. In order to address the above limitations, this study proposes a novel adversarial learning-based node-edge graph attention network (AL-NEGAT) for ASD identification based on multimodal MRI data. First, both node and edge features are modeled based on structural and functional MRI data to leverage complementary brain information and preserved in the constructed weighted adjacent matrix for individuals through the attention mechanism in the proposed NEGAT. Second, two AL methods are employed to improve the generalizability of NEGAT. Finally, a gradient-based saliency map strategy is utilized for model interpretation to identify important brain regions and connections contributing to the classification. Experimental results based on the public Autism Brain Imaging Data Exchange I (ABIDE I) data demonstrate that the proposed framework achieves a classification accuracy of 74.7% between ASD and typical developing (TD) groups based on 1007 subjects across 17 different sites and outperforms the state-of-the-art methods, indicating satisfying classification ability and generalizability of the proposed AL-NEGAT model. Our work provides a powerful tool for brain disorder identification.

Internet of things (IoT) driven structural health monitoring for enhanced seismic resilience: A rigorous functional analysis and implementation framework

Civil engineering infrastructures are increasingly becoming exposed to seismic loads, and at such a stage there is an increased need for alternative methods to fortify and enhance the resilience. The present study deals with the intricate linkage of internet of things (IoT) devices and the resilience of structures in a seismically affected zone. From the literature, the role of adaptive structural control, data acquisition and transmission, early warning systems, integration with building standards and codes, and real-time analytics and monitoring have been found to be the topmost aspects of ensuring resilience to seismic events. The seismically affected area of concern provides an in-depth analysis of the practical applications of the IoT in buildings in a seismic area. A survey was distributed to 239 respondents, and a very solid 58.99 % response rate proved the interest and participation of the respondents in the study. Using Partial Least Squares Structural Equation Modelling (PLS-SEM), the researchers frame a measurement model which is applied to analyze the reliability and validity of the IoT constructs. Further, the bootstrap resampling mechanisms are adopted to test the five hypotheses rigorously. The findings establish robust connections between adaptive structural control, data acquisition, early warning systems, integration with building codes and standards, real-time monitoring and analytics, and IoT, contributing to enhanced seismic resilience. Infrastructure developers can draw management implications from these findings, while empirical researchers can utilize the verified framework to guide future studies.

Application of the FRAME-IS to a multifaceted implementation strategy

BackgroundResearch demonstrates the importance of documenting adaptations to implementation strategies that support integration of evidence-based interventions into practice. While studies have utilized the FRAME-IS [Framework for Reporting Adaptations and Modifications for Implementation Strategies] to collect structured adaptation data, they are limited by a focus on discrete implementation strategies (e.g., training), which do not reflect the complexity of multifaceted strategies like practice facilitation. In this paper, we apply the FRAME-IS to our trial evaluating the effectiveness of PF on implementation fidelity of an evidence-based technology-facilitated team care model for improved hypertension control within a federally qualified health center (FQHC).MethodsThree data sources are used to document adaptations: (1) implementation committee meeting minutes, (2) narrative reports completed by practice facilitators, and (3) structured notes captured on root cause analysis and Plan-Do-Study-Act worksheets. Text was extracted from the data sources according to the FRAME-IS modules and inputted into a master matrix for content analysis by two authors; a third author conducted member checking and code validation.ResultsWe modified the FRAME-IS to include part 2 of module 2 (what is modified) to add greater detail of the modified strategy, and a numbering system to track adaptations across the modules. This resulted in identification of 27 adaptations, of which 88.9% focused on supporting practices in identifying eligible patients and referring them to the intervention. About half (52.9%) of the adaptations were made to modify the context of the PF strategy to include a group-based format, add community health workers to the strategy, and to shift the implementation target to nurses. The adaptations were often widespread (83.9%), affecting all practices within the FQHC. While most adaptations were reactive (84.6%), they resulted from a systematic process of reviewing data captured by multiple sources. All adaptations included the FQHC in the decision-making process.ConclusionWith modifications, we demonstrate the ability to document our adaptation data across the FRAME-IS modules, attesting to its applicability and value for a range of implementation strategies. Based on our experiences, we recommend refinement of tracking systems to support more nimble and practical documentation of iterative, ongoing, and multifaceted adaptations.Trial registrationClinicaltrials.gov NCT03713515, Registration date: October 19, 2018.

POT1 recruits and regulates CST-Polα/primase at human telomeres.

Telomere maintenance requires the extension of the G-rich telomeric repeat strand by telomerase and the fill-in synthesis of the C-rich strand by Polα/primase. At telomeres, Polα/primase is bound to Ctc1/Stn1/Ten1 (CST), a single-stranded DNA-binding complex. Like mutations in telomerase, mutations affecting CST-Polα/primase result in pathological telomere shortening and cause a telomere biology disorder, Coats plus (CP). We determined cryogenic electron microscopy structures of human CST bound to the shelterin heterodimer POT1/TPP1 that reveal how CST is recruited to telomeres by POT1. Our findings suggest that POT1 hinge phosphorylation is required for CST recruitment, and the complex is formed through conserved interactions involving several residues mutated in CP. Our structural and biochemical data suggest that phosphorylated POT1 holds CST-Polα/primase in an inactive, autoinhibited state until telomerase has extended the telomere ends. We propose that dephosphorylation of POT1 releases CST-Polα/primase into an active state that completes telomere replication through fill-in synthesis.

Hoffmann's sign in cervical spondylotic myelopathy patients: pathological insights from neuroimaging.

Hoffmann's sign testing is a commonly used physical examination in clinical practice for patients with cervical spondylotic myelopathy (CSM). However, the pathophysiological mechanisms underlying its occurrence and development have not been thoroughly investigated. Therefore, the present study aimed to explore whether a positive Hoffmann's sign (PHS) in CSM patients is associated with spinal cord and brain remodeling and to identify potential neuroimaging biomarkers with diagnostic value. Seventy-six patients with CSM and 40 sex- and age-matched healthy controls (HCs) underwent multimodal MRI. Based on the results of the Hoffmann's sign examination, patients were divided into two groups: those with a PHS (n = 38) and those with a negative Hoffmann's sign (NHS; n = 38). Quantification of spinal cord and brain structural and functional parameters of the participants was performed using various methods, including functional connectivity analysis, voxel-based morphometry, and atlas-based analysis based on functional MRI and structural MRI data. Furthermore, this study conducted a correlation analysis between neuroimaging metrics and neurological function and utilized a support vector machine (SVM) algorithm for the classification of PHS and NHS. In comparison with the NHS and HC groups, PHS patients exhibited significant reductions in the cross-sectional area and fractional anisotropy (FA) of the lateral corticospinal tract (CST), reticulospinal tract (RST), and fasciculus cuneatus, concomitant with bilateral reductions in the volume of the lateral pallidum. The functional connectivity analysis indicated a reduction in functional connectivity between the left lateral pallidum and the right angular gyrus in the PHS group. The correlation analysis indicated a significant positive association between the CST and RST FA and the volume of the left lateral pallidum in PHS patients. Furthermore, all three variables exhibited a positive correlation with the patients' motor function. Finally, using multimodal neuroimaging metrics in conjunction with the SVM algorithm, PHS and NHS were classified with an accuracy rate of 85.53%. This research revealed a correlation between structural damage to the pallidum and RST and the presence of Hoffmann's sign as well as the motor function in patients with CSM. Features based on neuroimaging indicators have the potential to serve as biomarkers for assessing the extent of neuronal damage in CSM patients.

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

Using a trie-based approach for storage and retrieval of goal-oriented plans in an S1/S2 cognitive architecture

In the last years, the System 1/System 2 cognitive architecture, proposed by psychologist Daniel Kahneman, raised the interest of many researchers in the field. System 1 is an intuitive, automatic, and fast-thinking system working effortlessly, without conscious effort. System 2 is a deliberate, analytical, and slower-thinking system employing conscious effort and attention. This work proposes an innovative approach that exploits techniques typical of information retrieval (the trie data structure) to efficiently encode the solutions’ repository at the border between System 2 and System 1. This repository stores the solutions (successful plans) the agent has already used and can re-enact to achieve the goals. System 2 conceives new plans and delegates System 1 to execute them. If the plan is successful (and so it becomes a solution), System 1 stores that in the repository to quickly retrieve any solution that may help fulfil the goals deliberated by System 2 in the future.

Lake Environmental Data Harvester (LED) for Alpine Lake Monitoring with Autonomous Surface Vehicles (ASVs)

This article introduces the Lake Environmental Data Harvester (LED) System, a robotic platform designed for the development of an innovative solution for monitoring remote alpine lakes. LED is intended as the first step in creating portable robotic tools that are lightweight, cost-effective, and highly reliable for monitoring remote water bodies. The LED system is based on the Shallow-Water Autonomous Multipurpose Platform (SWAMP), a groundbreaking Autonomous Surface Vehicle (ASV) originally designed for monitoring wetlands. The objective of LED is to achieve the comprehensive monitoring of remote lakes by outfitting the SWAMP with a suite of sensors, integrating an IoT infrastructure, and adhering to FAIR principles for structured data management. SWAMP’s modular design and open architecture facilitate the easy integration of payloads, while its compact size and construction with a reduced weight ensure portability. Equipped with four azimuth thrusters and a flexible hull structure, SWAMP offers a high degree of maneuverability and position-keeping ability for precise surveys in the shallow waters that are typical of remote lakes. In this project, SWAMP was equipped with a suite of sensors, including a single-beam dual-frequency echosounder, water-quality sensors, a winch for sensor deployment, and AirQino, a low-cost air quality analysis system, along with an RTK-GNSS (Global Navigation Satellite System) receiver for precise positioning. Utilizing commercial off-the-shelf (COTS) components, a Multipurpose Data-Acquisition System forms the basis for an Internet of Things (IoT) infrastructure, enabling data acquisition, storage, and long-range communication. This data-centric system design ensures that acquired variables from both sensors and the robotic platform are structured and managed according to the FAIR principles.

Laser spectroscopy of magnetic splitting of Pr I levels in the presence of saturation effects

Laser spectroscopy is a very precise tool in determining atomic structure data. However, in the case of complex spectra, when the individual components of the spectra cannot be separated, precise analysis is hindered by the appearing saturation effect. Sometimes this effect is so large that the spectrum simulation does not provide precise results. In the present work, we present the analysis of the Zeeman spectra of seven praseodymium lines in the range of 566–652 nm, which were recorded using the LIF technique in the presence of the saturation effect. To analyze these spectra, we used a specially dedicated computer program, which allowed for very accurate matching of the simulated spectrum to the recorded experimental shape. As a result of the conducted research, we determined 14 Lande factor values, 3 of which are new.Graphic

Unstructured EMR data hold the key to oncology studies: Methods for validating NLP-extracted phenotypes from EMR notes.

11133 Background: Natural language processing (NLP) parsers are advanced algorithms designed to parse values of interest from free-text stored in Electronic Medical Records (EMR). Given the importance of information stored in free-text for use in oncology research, it is crucial to understand how accurately NLP algorithms are extracting oncology-specific measures of interest. We focus on validating NLP-parsers that identify three measures critical to oncology research by evaluating how closely the NLP-parser results match manual chart abstractor review: AJCC summary cancer stage group (cancer stage), AJCC TNM stage (TNM stage), and surgical treatment of cancer (surgery). Methods: Following deployment of the parsers on 8,000 non-identifiable free-text notes housed in an aggregated non-identifiable U.S. EMR database, manual chart abstraction was performed on a random selection of notes (n=300) dated between 2008 to 2023 to validate the parsers’ accurate capture of cancer stage, TNM stage, and surgical treatment of cancer. We report true positives (TP), false positives (FP) and positive percent value (PPV). Results: The PPV of AJCC Cancer Stage was 99.0% (n=297 TP, n=3 FP). The PPV of the TNM stage NLP-parser was 97.3% for tumor stage (n=292 TP, n=8 FP), 99.0% for nodal stage (n=297 TP, n=3 FP), and 96.7% for presence of metastases (n=290 TP, n=10 FP). The PPV of the surgery parser was 87.8% (n=468 TP, n=65 FP). Conclusions: These NLP-parsers performed very well identifying the measures of interest, providing confidence in use of these derived measures which are central to oncology research. Validation is a necessary initial step in processing real-world data for use in real-world evidence generation, with unique considerations needed in the oncology research space due to key information being documented in EMR free-text rather than structured data fields.

Efficient Data Structures &amp; Algorithms Hunter: A Study in Optimization

Abstract: Data Structures and Algorithms is a fundamental course in Computer Science. However, many students find it difficult because it requires abstract thinking. It would be very helpful if there was a visualization tool of data structures such as arrays, queues, stacks, trees, and graphs for students to manipulate. The tool would allow students to see how an element is inserted into or deleted from different data structures, how a tree is traversed in different order (preorder, in-order, post-order, level-order), etc. Moreover, this tool would provide a simple language, by which students can write their own algorithms so that the execution of the algorithm is animated. This project is intended to create an exploration environment, in which students can learn through experimentation. This tool can be used as an effective supplement to the traditional classroom education and textbook for Data Structures and Algorithms courses. In this paper, a software application that features the visualization of commonly used data structures and their associated insertion and deletion operations is introduced. In addition, this software can be used to animate user-defined algorithms.

Comparing Generic Paediatric Health-Related Quality-of-Life Instruments: A Dimensionality Assessment Using Factor Analysis.

Widely used generic instruments to measure paediatric health-related quality of life (HRQoL) include the EQ-5D-Y-5L, Child Health Utility 9 Dimension (CHU-9D), Paediatric Quality of Life Inventory (PedsQL) and Health Utilities Index (HUI). There are similarities and differences in the content of these instruments, but there is little empirical evidence on how the items they contain relate to each other, and to an overarching model of HRQoL derived from their content. This study aimed to explore the dimensionality of the instruments using exploratory factor analysis (EFA). Data from the Australian Paediatric Multi-Instrument Comparison (P-MIC) Study were used. EQ-5D-Y-5L, CHU-9D, PedsQL and HUI data were collected via proxy or child self-report data. EFA was used to investigate the underlying domain structure and measurement relationship. Items from the four instruments were pooled and domain models were identified for self- and proxy-reported data. The number of factors was determined based on eigenvalues greater than1. A correlation cut-off of 0.32 was used to determine item loading on a given factor, with cross-loading also considered. Oblique rotation was used. Results suggest a six-factor structure for the proxy-reported data, including emotional functioning, pain, daily activities, physical functioning, school functioning, and senses, while the self-report data revealed a similar seven-factor structure, with social functioning emerging as an additional factor. We provide evidence of differences and similarities between paediatric HRQoL instruments and the aspects of health being measured by these instruments. The results identified slight differences between self- and proxy-reported data in the relationships among items within the resulting domains.

The working alliance inventory – short version: psychometric properties of the patient and therapist form in youth mental health and addiction care

The therapeutic alliance is considered to play an important role in youth treatment. The commonly used versions of the Working Alliance Inventory (WAI) are based on Bordin’s three-dimensional alliance model. However, previous psychometric studies of the WAI did not find this three-dimensional structure in youth psychotherapy. These earlier findings may indicate different perceptions of the alliance by adolescent versus adult patients, but may also be due to methodological shortcomings. The current study aims to address previous study limitations by evaluating the factor structure of the short version of the WAI (WAI-S) in youth treatment in multilevel analysis to address the hierarchical structure of the alliance data. We examined the psychometric properties of the patient (n = 203) and therapist (n = 62) versions of the WAI-S in youth mental health and addiction care and tested four multilevel models of alliance at start of treatment and 2-month follow-up. Our results suggests a two-factor model for youth and a three-dimensional model for their therapist at both time points. Since this is the first study that finds a best fit for a two-dimensional construct of alliance in youth, more research is needed to clarify whether the differences in alliance dimensions are due to measurement differences between the WAI-S for youth and therapists or whether youth and their therapists truly differ in their perceptions of the concept of alliance.

DSTree: A Spatio-Temporal Indexing Data Structure for Distributed Networks

DSTree: A Spatio-Temporal Indexing Data Structure for Distributed Networks