Representation Of Structure Research Articles

Evaluation of a Two-Week Gait Sonification Intervention in Individuals 12 weeks After Total Knee Replacement: A Case Study

ABSTRACT This case study aimed to examine the effects and perception of a two-week gait feedback intervention via sonification of the sagittal knee angle in individuals 12 weeks after total knee replacement surgery. Five individuals (1 male, 4 females, mean age: 61.2 years) underwent five 10-min feedback sessions within two to 3 weeks. Gait symmetry (as Ratio Index) and cognitive representation (via structural dimensional analysis-SDA-M) were assessed in the pre- and posttest. Additionally, guided interviews were conducted focusing on changes in gait perception, attention on gait execution, and self-perceived feedback efficacy. Four individuals completed the study. Mean ratio indexes were smaller after the intervention. The cognitive representation structure was closer to functional clustering (stance and swing) compared to before the intervention. The interviews revealed that for all participants (except one), gait sonification was perceived helpful to improve their gait symmetry, noticing a change in attentional processes while walking and also in gait perception. Gait sonification seems promising to help individuals after knee joint replacement improving their gait by providing an additional acoustic reference. More research is needed in the field of psychoacoustics combined with movement science to further explore the general benefits of this feedback method. For gait rehabilitation, future studies in larger samples and at different stages of rehabilitation can help to apply this method efficiently to different patient groups.

The medial and lateral orbitofrontal cortex jointly represent the cognitive map of task space

A cognitive map is an internal model of the world’s causal structure, crucial for adaptive behaviors. The orbitofrontal cortex (OFC) is central node to decision-making and cognitive map representation. However, it remains unclear how the medial OFC (mOFC) and lateral OFC (lOFC) contribute to the formation of cognitive maps in humans. By performing a multi-step sequential task and multivariate analyses of functional magnetic resonance imaging (fMRI) data, we found that the mOFC and lOFC play complementary but dissociable roles in this process. Specifically, the mOFC represents all hidden task state components. The lOFC and dorsolateral prefrontal cortex (dlPFC) encode abstract rules governing structure knowledge across task states. Furthermore, the two orbitofrontal subregions are functionally connected to share state-hidden information for constructing a representation of the task structure. Collectively, these findings provide an account that can increase our understanding of how the brain constructs abstract cognitive maps in a task-relevant space.

Anti-Politics Behaviour among Young Malaysian Chinese

Objective: The study investigated the anti-politics behaviour of Chinese youth in Malaysia. Method: Semi-structured interviews were conducted with 21 Chinese participants aged 18 to 40 in Malaysia. They were from lower socio-economic backgrounds. The interview questions included frequency of reading newspapers, and having political conversations with friends, parents and educators. They were also asked about their interest in political speeches, and political involvement. Thematic analysis was conducted for the 85,000-word interview transcript. Results: The interview results revealed that the participants read newspaper articles and social media postings to confirm their pre-existing opinions on political issues. All the participants read Chinese newspapers because they were Chinese-educated, but only three participants were politically informed. The agents of political socialisation were their parents rather than peers and educators. Only one-third of the young Chinese participants read political news, and engaged in political conversations. They were likely to vote, hope for change, and believe that politics is a force for food. On the other hand, two-thirds of the participants were not interested in politics. The anti-politics behaviour was due to the politicians’ negative behaviour and observations of dirty tactics in politics. Most of the participants exhibited non-participation without democracy, and there were few participants in the political participation category, whether negative evaluation or positive evaluation. Conclusion: The study indicates that information black-out contributed to apathy of Chinese youth towards political participation and the disengagement may limit Chinese representation in the political structures of Malaysia in the future.

The sharp lower bound of tricyclic graphs with respect to the ISI index: applications in octane isomers and benzenoid hydrocarbons.

The inverse sum indeg index of a given graph G is symbolized with ISI and defined by sum of the weights entire links . We denote (resp. ) the degree of a vertex u (resp. v) of G. In this paper, we obtained the sharp lower bound of tricyclic graphs with respect to the ISI index of order with size . By using atoms as vertices and chemical bonds as edges, graph theory permits the representation of molecular structures as mathematical entities called graphs. Based on the above concept, we formulated the ISI index of octane isomers (OI) and benzenoid hydrocarbons (BH) and compared the values of ISI index with various degree-based TI's via their correlations and chemical properties. The structural analysis of octane isomers is an important application of this research, as the ISI index delivers insights into stability designs across various isomeric forms.

Singularity-free dark energy star in Rastall gravity

The notion of dark energy presents a potential avenue to avoid the gravitational collapse of compact objects, leading to singularities. The present article is devoted primarily to study a novel singularity-free relativistic solution of the Einstein field equations for dark energy stars in the framework of the Rastall theory of gravity. To analyse our model, we consider the Low-Mass X-ray Binary (LMXB) 4U 1608-52 having a mass of 1.74M⊙ and a radius of 9.3 Km (Güver et al., 2010) as a possible dark energy star candidate. We begin with the dark energy equation of state, where the density of dark energy is proportional to the isotropic perfect fluid distribution through a coupling parameter α. We compute the induced metric and extrinsic curvature tensors at the stellar hyper-surface to evaluate the necessary unknown constants appearing in the model. We conduct a thorough and comprehensive analysis to demonstrate the dependence of the physical behaviour of the model on the Rastall parameter (ξ), and interestingly, we obtain a possible phase transition from the dark to baryonic profile, which is sensitive to both α and ξ. We also compute the percentage of dark energy present in the model by varying ξ. However, for a fixed value of ξ, the percentage variation of dark energy with the mass of a star shows that the percentage of dark energy depends on the mass and radius of a star. The causality and energy conditions are well met in the present model, which describes its physical acceptability. The stability of the stellar configuration is established through the study of stability analysis. The graphical nature of the physical parameters and the present theoretical study show that our proposed model is non-singular and a viable representation of a stable realistic stellar structure in the presence of dark and baryonic matter simultaneously.

New insights into the crustal structures of the Arabian Plate and surrounding plates unveiled from the crustal thickness model and its implications for geodynamics

Geodynamics of the Arabian Peninsula is less understood as crustal structures are not well delineated due to uneven distribution and lack of seismic measurements and ground based geophysical data in the Arabian interior. To address this, we delineated crustal structures in the Arabian Peninsula through the creation of a crustal thickness model from the inversion of GOCO60s gravity field model, which revealed details on the underlying crustal structures. Our model revealed strong crustal thickness variations in the Zagros fold belt that indicates high deformational history and crustal reworked, which also reflect directional collisional events between the Arabian and Eurasian Plates, similar to the Himalaya type as the result of mantle processes. The resolved crustal structures also reveal a relatively slow rifting process on the southern end of the Red Sea than at the northern end, which resulted in the bulging of crust in the southern part of Zagros Fold Mountains. Further, folding and bulging of crustal structures is more intense near the Arabian Platform and slower on the Eurasian side, which could indicate a relatively stable Eurasian plate compared to the geodynamically active Arabian Plate. Overall improvement in our high-resolution crustal thickness model yields an improved representation of crustal structures over previously derived models of the Arabian Peninsula and the Surrounding plates that indicates a variation in the Proterozoic crust in Arabian Plate, possibly indicating a secular variation in the crustal structure.

Application of monomer structures and fragments of local symmetry for simulation of glass transition temperatures of polymers

ABSTRACT A scheme for constructing models of the ‘structure-glass transition temperature of a polymer’ is proposed. It involves the representation of the molecular structure of a polymer through the architecture of monomer units represented through a simplified molecular input-line entry system (SMILES) and the fragments of local symmetry (FLS). The statistical quality of such models is quite good: the determination coefficient values for active training set, passive training set, calibration set, and validation set are 0.711, 0.715, 0.859, and 0.884, respectively. The reliability of the approach was assessed for three random distributions of experimental data in the training and validation sets. Machine learning technique was used for a structured training sample distributed in so-called active and passive learning, combined with a calibration set. The optimal descriptors for developed the models were calculated by the Monte Carlo technique.

Enhancing U-Net-based Pseudo-CT generation from MRI using CT-guided bone segmentation for radiation treatment planning in head & neck cancer patients.

This study investigates the effects of various training protocols on enhancing the precision of MRI-only Pseudo-CT generation for radiation treatment planning and adaptation in head & neck cancer patients. It specifically tackles the challenge of differentiating bone from air, a limitation that frequently results in substantial deviations in the representation of bony structures on Pseudo-CT images. The study included 25 patients, utilizing pre-treatment MRI-CT image pairs. Five cases were randomly selected for testing, with the remaining 20 used for model training and validation. A 3D U-Net deep learning model was employed, trained on patches of size 643with an overlap of 323. MRI scans were acquired using the Dixon gradient echo (GRE) technique, and various contrasts were explored to improve Pseudo-CT accuracy, including in-phase, water-only, and combined water-only and fat-only images. Additionally, bone extraction from the fat-only image was integrated as an additional channel to better capture bone structures on Pseudo-CTs. The evaluation involved both image quality and dosimetric metrics. The generated Pseudo-CTs were compared with their corresponding registered target CTs. The mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) for the base model using combined water-only and fat-only images were 19.20 ± 5.30 HU and 57.24 ± 1.44 dB, respectively. Following the integration of an additional channel using a CT-guided bone segmentation, the model's performance improved, achieving MAE and PSNR of 18.32 ± 5.51 HU and 57.82 ± 1.31 dB, respectively. The dosimetric assessment confirmed that radiation treatment planning on Pseudo-CT achieved accuracy comparable to conventional CT. The measured results are statistically significant, with ap-value < 0.05. This study demonstrates improved accuracy in bone representation on Pseudo-CTs achieved through a combination of water-only, fat-only and extracted bone images; thus, enhancing feasibility of MRI-based simulation for radiation treatment planning.

A three-parameter model for dark matter halos based on general relativity and quantum field theory

In this paper, we present a three-parameter model for the distributions of “dark matter” around local spherically symmetric and static distributions of normal matter, usually referred to as “dark matter halos”. The model is based on the assumption that the spontaneous symmetry breaking mechanism involving the Higgs field in the standard model of elementary particle physics leads to the existence of a constant distribution of vacuum energy throughout space–time. This is to be interpreted as a constant distribution of proper energy, as measured by local observers in their proper reference frames. Due to the presence of this universal distribution of vacuum energy, the model requires the introduction of the cosmological term in the Einstein field equations, in order to regulate the behavior of the resulting geometry at cosmologically large distances. By implementing the model on galactic scales, we are able to establish the gravitational consequences of such a homogeneous distribution of proper energy density at these scales. This results in equivalent halo masses and orbital velocity curves that, at least qualitatively, match the observations for galactic “dark matter” halos. Although the detailed realization of the model presented and calculated here cannot be construed as a precise representation of galactic structure, given that most of the actual structure of the galaxy, such as the galactic disk itself, is ignored, and replaced by a single spherically symmetric bulge, in this paper we do introduce the conceptual framework that may be used to construct a more complete galactic model in the future.

Developing pangenomes for large and complex plant genomes and their representation formats.

The development of pangenomes has revolutionized genomic studies by capturing the complete genetic diversity within a species. Pangenome assembly integrates data from multiple individuals to construct a comprehensive genomic landscape, revealing both core and accessory genomic elements. This approach enables the identification of novel genes, structural variations, and gene presence-absence variations, providing insights into species evolution, adaptation, and trait variation. Representing pangenomes requires innovative visualization formats that effectively convey the complex genomic structures and variations. This review delves into contemporary methodologies and recent advancements in constructing pangenomes, particularly in plant genomes. It examines the structure of pangenome representation, including format comparison, conversion, visualization techniques, and their implications for enhancing crop improvement strategies. Earlier comparative studies have illuminated novel gene sequences, copy number variations, and presence-absence variations across diverse crop species. The concept of a pan-genome, which captures multiple genetic variations from a broad spectrum of genotypes, offers a holistic perspective of a species' genetic makeup. However, constructing a pan-genome for plants with larger genomes poses challenges, including managing vast genome sequence data and comprehending the genetic variations within the germplasm. To address these challenges, researchers have explored cost-effective alternatives to encapsulate species diversity in a single assembly known as a pangenome. This involves reducing the volume of genome sequences while focusing on genetic variations. With the growing prominence of the pan-genome concept in plant genomics, several software tools have emerged to facilitate pangenome construction. This review sheds light on developing and utilizing software tools tailored for constructing pan-genomes in plants. It also discusses representation formats suitable for downstream analyses, offering valuable insights into the genetic landscape and evolutionary dynamics of plant species. In summary, this review underscores the significance of pan-genome construction and representation formats in resolving the genetic architecture of plants, particularly those with complex genomes. It provides a comprehensive overview of recent advancements, aiding in exploring and understanding plant genetic diversity.

Putting the prime in priming: Using prime processing behavior to predict target structural processing.

Structural priming effects are widespread and heavily relied upon to assess structural representation and processing. Whether these effects are caused by error-driven implicit learning, residual activation, a combination of these, or some other learning mechanism remains to be established. The current study used preexisting data and a novel data analysis approach that links processing at the prime to later processing at the target to better understand the nature of structural priming. This novel analytic approach was applied to total reading times from a previously published structural priming study in comprehension, which provided processing measures of the structurally critical regions of prime reduced-relative clause sentences. These were then used as predictors in a series of hierarchical linear models where analogous processing measures at the target sentence regions served as outcome variables. Separate sets of models were run for prime-target pairs that had the same structure (i.e., abstract priming) and those that had the same structure and initial verb (i.e., a lexical boost). Prime-to-target processing relationships were observed for both types of prime-target pairs, but showed very different patterns. This provides support for the claim that abstract priming effects and the lexical boost are caused by different mechanisms. Additionally, the observed effects were positive and so do not support the error-driven learning prediction that processing difficulty at the prime should lead to greater facilitation at the target. Overall, this novel method provides a new tool for investigating structural priming and processing.

Positive polarity items: an illusion of ungrammaticality

ABSTRACT Negative Polarity Item (NPIs) produce an illusion of grammaticality in some contexts with negation. Many approaches to modelling the NPI illusion propose that it is driven by the processor's attempt to link an NPI to a negative element. We investigate an illusion effect observed with Positive Polarity Item (PPIs), another class of polarity sensitive element. While NPIs must be licensed by a negative element, PPIs are anti-licensed by negative elements. We find an illusion of ungrammaticality for PPIs in environments where an illusion of grammaticality is observed for NPIs. Thus, we argue there is a general polarity illusion. We find that several accounts of the NPI illusion either predict this PPI illusion or can capture this effect with a straightforward extension. The approaches which are able to predict this effect share a reliance on structural representation, highlighting the importance of both the licensing features of polarity items and the structural detail in sentence processing representations.

Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics.

Identifying informative low-dimensional features that characterize dynamics in molecular simulations remains a challenge, often requiring extensive manual tuning and system-specific knowledge. Here, we introduce geom2vec, in which pretrained graph neural networks (GNNs) are used as universal geometric featurizers. By pretraining equivariant GNNs on a large dataset of molecular conformations with a self-supervised denoising objective, we obtain transferable structural representations that are useful for learning conformational dynamics without further fine-tuning. We show how the learned GNN representations can capture interpretable relationships between structural units (tokens) by combining them with expressive token mixers. Importantly, decoupling training the GNNs from training for downstream tasks enables analysis of larger molecular graphs (that can represent small proteins at all-atom resolution) with limited computational resources. In these ways, geom2vec eliminates the need for manual feature selection and increases the robustness of simulation analyses.

Comparative study of degree and neighborhood degree sum-based topological indices for predicting physicochemical properties of skin cancer drug structures

Quantitative structure property relationship (QSPR) is essential in rational drug design by facilitating the prediction, optimization, and prioritization of potential drug candidates based on their chemical structures and properties, optimizing the utilization of computational resources and minimizing costs. Topological indices play a fundamental role in QSPR studies by providing a quantitative representation of molecular structures and facilitating the prediction of various chemical properties and activities. Metastatic skin cancer can be aggressive and potentially fatal, and thus developing effective drugs can improve patient outcomes, including overall survival rates and progression-free survival. This research work focuses on the structural behaviors of medications used in the treatment of skin cancer, including binimetinib, fisetin, encorafenib, picato, fluorouracil, trametinib, vemurafenib, imiquimod, odomzo, vismodegib, dacarbazine, cobimetinib, dabrafenib, sesamol, curcumin, doxorubicin, temozolomide, paclitaxel, itraconazole, and hyaluronan. We have developed QSPR models involving degree and neighborhood degree sum-based indices and conducted a comparative analysis to highlight the efficacy of the models.

Virtual 3D reconstruction of complex congenital cardiac anatomy from 3D rotational angiography

BackgroundDespite advancements in imaging technologies, including CT scans and MRI, these modalities may still fail to capture intricate details of congenital heart defects accurately. Virtual 3D models have revolutionized the field of pediatric interventional cardiology by providing clinicians with tangible representations of complex anatomical structures. We examined the feasibility and accuracy of utilizing an automated, Artificial Intelligence (AI) driven, cloud-based platform for virtual 3D visualization of complex congenital heart disease obtained from 3D rotational angiography DICOM images.MethodsFive patients selected at random with 3DRA performed in the pediatric cardiac catheterization suite were selected. 3DRA’s were performed following published institutional protocols and segmentations performed by primary operators. The 3DRA DICOM images were anonymized as per protocol and exported. Images when then processed by Axial3D Artificial Intelligence (AI) driven cloud-based platform for virtual segmentation. Two separate expert operators were selected to subjectively analyze the segmentations and compare them to the operator reconstructions for anatomic accuracy.ResultsComparing results with local reconstructions by expert operators, five different patient anatomies were analyzed, showcasing Axial3D’s ability to produce highly detailed reconstructions with improved visual appeal, including color-coded segments for implanted materials like stents. The reconstructions exhibited superior segmentation of different intrathoracic structures when compared to local models, offering valuable insights for medical professionals and patients.ConclusionsThe use of the AI driven, cloud-based platform for 3D visualization of complex congenital heart lesions presents a promising advancement in pediatric interventional cardiology, facilitating enhanced patient care, procedural planning, and educational opportunities for trainees and patients alike.

Cognitive representation of gait: differences in memory structures between individuals after total knee arthroplasty and total hip arthroplasty.

The objective was to examine differences in the gait-specific cognitive representation structures between individuals after total knee- (TKA) and after total hip-joint arthroplasty (THA). The cognitive representation structure was compared between three groups: 1. three months after TKA (n = 12), 2. three months after THA (n = 12), and 3. healthy control group (CG) (n = 12) using the structural dimensional analysis of mental representation (SDA-M). Additionally, perceived joint function was rated by either the KOOS, JR. or HOOS, JR. Mean distribution of perceived joint function was not significantly different between the TKA (60.35 ± 11.2) and THA group (68.01 ± 13.8) (t = - 1.425; p = .173). In the cognitive representation structure, the THA group exhibited functional differences from the TKA group and control group, both of which showed a functional structure. Three months after hip joint replacement the gait-specific cognitive representation structure seems to reflect joint function-specific deviations. Therefore, focussing on functional recovery of cognitive gait representation may facilitate gait rehabilitation in individuals after hip replacement.

Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds

Accurate and efficient path planning in indoor environments relies on high-quality navigation networks that faithfully represent the spatial and semantic structure of the environment. Three-dimensional semantic point clouds provide valuable spatial and semantic information for navigation tasks. However, extracting detailed navigation networks from 3D semantic point clouds remains a challenge, especially in complex indoor spaces like staircases and multi-floor environments. This study presents a comprehensive framework for developing and extracting robust navigation network models, specifically designed for indoor path planning applications. The main contributions include (1) a preprocessing pipeline that ensures high accuracy and consistency of the input semantic point cloud data; (2) a moving window algorithm for refined node extraction in staircases to enable seamless navigation across vertical spaces; and (3) a lightweight, JSON-based storage structure for efficient network representation and integration. Additionally, we presented a more comprehensive sub-node extraction method for hallways to enhance network continuity. We validated the method using two datasets—the public S3DIS dataset and the self-collected HoloLens 2 dataset—and demonstrated its effectiveness through Dijkstra-based path planning. The generated navigation networks supported practical scenarios such as wheelchair-accessible path planning and seamless multi-floor navigation. These findings highlight the practical value of our approach for modern indoor navigation systems, with potential applications in smart building management, robotics, and emergency response.

Chinese College Students' Cognitive Biases Toward Suicide Prevention and Associated Strategies to Improve Life Education

ABSTRACTLife education is an essential element of suicide prevention in colleges. However, the existing practice of life education in China may be insufficient to match the new spectrum of students' knowledge needs. The study exemplified the participants' responses to common suicide myths with a 3‐year follow‐up investigation (Study 1), and the optimal strategies individuals chose to manage the people in crisis in a hypothetical scenario (Study 2). The results revealed that generally, college students in China have mature cognitive attitudes toward suicide‐related issues, and have systematic structural representations of crisis intervention. However, there are some prominent cognitive biases, mainly focusing on concerns in the process of communication and evaluation, difficulty in considering suicide as a complex moral phenomenon, and insufficient awareness of problem orientation in crisis interventions. To match the students' new cognitive changes and modern education's higher moral requirements, the optimization direction should focus on the response to students' personalized cognitive reality by adding meta‐suicidology reflection, learning ethical reduction in case analysis, and cultivating problem‐solving skills.

Real-time deformable structure tracking in 3D ultrasound sequences using deformable convolutional layers.

Ultrasound imaging can provide 3D images of soft tissue structures in real-time without harmful radiation. Due to its high level of availability and low-cost characteristics, it is becoming more and more interesting for therapy guidance purposes like in radiotherapy. However, for usage in radiotherapy a robust and real-time image analysis method is required to be able to track the target during the treatment session. Soft tissue structures move in high dimensional motion patterns, including deformation especially due to breathing which makes the tracking task challenging. To overcome the deformation complexity, a novel real-time capable tracking approach in 3D ultrasound is proposed in this study. Deformable convolution layers are included in a 2D convolutional autoencoder architecture to learn deformation-invariant representations from ultrasound patches. For this, a novel 3D to 2D ultrasound patch reduction strategy is proposed. The tracking procedure is performed in the representation space of the autoencoder. We therefore implemented a greedy local search tracking algorithm and evaluated it in a preliminary study on the basis of nine expert-labeled landmarks in 18 in-vivo 3D ultrasound liver sequences. Four different autoencoder architectures with different deformable convolution arrangements are compared in the evaluation. The results show that using deformable convolution layers is beneficial compared to conventional convolution layers. A mean tracking error of 1.58 ± 0.87 mm was measured using deformable convolution, which is an improvement by 10.7% compared to conventional convolution. Additionally, evaluating the runtime shows that the tracking algorithm is real-time capable as the mean runtime per 3D ultrasound frame was around 4 ms. It could be shown that using deformable convolution layers is beneficial for learning meaningful representations of deformable structures from ultrasound patches. A tracking error similar to state-of-the-art methods could be achieved but in a runtime up to 100 times faster.

Application of mobile laser scanner to augment stationary collected point clouds

ABSTRACT Acquiring precise and comprehensive representations of architectural structures or industrial installations is crucial for a variety of monitoring and cadastral applications. A number of tools have been developed for this purpose, but they often face the problem with balancing accuracy and acquisition time. Terrestrial laser scanners typically provide the highest precision in point clouds, as they perform scanning while motionlessly mounted on rigid tripods. Careful pre-planning is needed for scanner locations on the object under investigation. Limited number of scanning locations often compromises point clouds at inaccessible locations, especially in complex environments. In this paper, we propose a solution to this issue by employing an autonomous quadrupedal robot-based laser scanner to augment the point clouds obtained with high-precision stationary scanners. We demonstrate this approach in an auditorium consisting of stairs, multi-row seats and additional complex-shaped acoustic diffusors.