Precise Matching Research Articles

Reconstruction Strategy for Upper Extremity Defects After Bone Tumor Resection Based on 3D Customized Bone Cement Mold.

Reconstructing bone defects in the upper extremities and restoring their functions poses a significant challenge. In this study, we describe a novel workflow for designing and manufacturing customized bone cement molds using 3D printing technology to reconstruct upper extremity defects after bone tumor resection. Computer tomography data was acquired from the unaffected upper extremities to create a detachable mold, which can be customized to fit the joint precisely by shaping the bone cement accordingly. Fourteen patients who underwent reconstructive surgery following bone tumor resection in the proximal humerus (13 cases) or distal radius (1 case) between January 2014 and December 2022 were retrospectively evaluated. The medical records of this case series were reviewed for the demographic, radiological, and operative data. Metastasis, local recurrence, and complication were also reviewed. Additionally, Musculoskeletal Tumor Society Score (MSTS) and Visual Analogue Scale (VAS) were used to assess clinical outcomes. The mean follow-up period was 49.36 ± 15.18 months (range, 27-82 months). At the end of follow-up, there were no cases of metastasis or recurrence, and patients did not experience complications such as infection, dislocation, or implant loosening. Two cases complicated with subluxation (14.3%), and 1 case underwent revision surgery for prosthetic fracture (7.1%). The average MSTS score was 23.2 ± 1.76 (77.4%, range, 66.7%-86.7%), and the postoperative VAS score was 1.86 ± 1.03 (range, 1-4), which was significantly lower than that before surgery (average preoperative VAS score was 5.21 ± 2.00 (range, 2-8)) (P < .001). Customized 3D molds can be utilized to shape bone cement prostheses, which may serve as a potential alternative for reconstructing the proximal humerus and distal radius following en bloc resection of bone tumors. This reconstruction strategy offers apparent advantages, including precise matching of articular surfaces and comparatively reduced costs.

4D-printed dual-responsive bioscaffolds for treating critical-sized irregular bone defects

Currently, treating irregular bone defects with a critical size is still challenging as material implantation in the complex defects with irregular shapes, effective osteogenesis and sufficient vascularization cannot be easily achieved simultaneously via a simple strategy. Herein, a dual-responsive bone tissue engineering scaffold is developed using a 4D printing strategy by intergrating a single type of multifunctional magnetic nanoparticles, Fe3O4@SiO2, with printing inks made of bioceramics and biopolymers. Minimally invasive implantation, fluent navigation of scaffolds as well as the precise boundary matching between scaffolds and the irregular defects are achieved through near-infrared (NIR) irradiation-based temperature responsive shape recovery and static magnetic field (SMF) stimulation. Moreover, improved bone regeneration in critical-sized bone defects is successfully achieved through the activation of PI3K/AKT pathway which significantly promotes osteogenic differentiation and vascularization. Besides, NIR-based photothermal stimulation upregulates the expression of heat shock protein (HSP90) and further promotes the osteogenesis and vascularization. The in vivo study also confirms that our scaffold can precisely fit the bone defect, and induce satisfactory osteogenesis and angiogenesis. This work provides a facile strategy to simultaneously-realize easy scaffold implantation in irregular bone defects and improves osteogenesis and angiogenesis by integrating a single type of multifunctional nanoparticles with scaffold matrices.

Potential prediction in aqueous organic redox-targeting flow batteries: DFT calculation and experimental validation

Aqueous organic redox flow batteries (AORFBs) face challenges of low energy density, which can be addressed by the strategy of redox-targeting (RT) reaction integrating solid materials (SMs) with redox mediators (RMs). However, the potential matching between SM and RM is demanding and complex. In this work, we establish a precise density functional theory (DFT) protocol to predict redox potential in RT-AORFB with anthraquinone-2,7-disulfonic acid (AQDS) derivatives as RM and poly(N-anthraquinoyl pyrrole) (PAQPy) as SM. Theoretical redox potentials are calculated from the Gibbs free energy (GFE) of various molecular models. The results suggest a precise potential match for 1,8-dihydroxyanthraquinone-2,7-disulfonic acid (1,8-DHAQDS) and PAQPy (-1.08 V and -1.09 V vs. SHE). Additionally, hydrogen bonding is involved to make simulation results more realistic, demonstrating a positive potential shift with increased GFE difference for both AQDS derivatives and PAQPy. To further elucidate the influencing mechanism of hydrogen bonding, electrostatic potential (ESP) and HOMO-LUMO gap are integrated together with GFE. The results indicate the introduction of hydrogen bonding results in extended distance for electron tunneling and a larger HOMO-LUMO gap, leading to higher GFE difference and a positive potential shift. Remarkably, results of experimental validations agree well with theoretical potential calculation. Based on predictions results, the RT-AORFB is successfully constructed with the well-matched 1,8-DHAQDS and PAQPy, exhibiting a 3.86-fold capacity enhancement compared to blank AORFB with 1,8-DHAQDS. The integrated DFT approach with GFE, ESP and HOMO-LUMO gap in this work emerges as a promising method for accurately predicting redox potentials in RT systems.

Esthetic Rehabilitation of Congenitally Peg-Shaped Lateral Incisors Using the Injectable Composite Resin Technique: A Clinical Report

ABSTRACT This report details the esthetic dental rehabilitation of a patient with peg-shaped and malformed lateral incisors, conditions that stem from congenital anomalies in tooth formation, using the injectable composite resin technique (ICRT). The ICRT clinical workflow initiates with a smile analysis and precise shade matching, followed by the construction of a diagnostic wax-up crafted according to esthetic and functional guidelines. Subsequently, a mock-up is developed to ensure that the planned restorations fulfill the patient’s functional requirements and esthetic expectations. A transparent silicone index is employed to accurately transfer the patient-approved wax-up into the mouth, utilizing injectable flowable composite resin. This method enhances treatment predictability and ensures that the final direct restoration precisely reflects the approved design within a reasonable chairside timeframe.

Study of fracture monitoring and heat extraction evaluation in geothermal reservoir modified by abandoned well pattern: Numerical models and case studies

The abandoned well pattern for transforming geothermal reservoirs aligns seamlessly with the prevailing shift towards low carbon and enhanced energy efficiency. The effect of anomalous fractures on the effectiveness of heat production is not clear. In this study, we introduce the modified pressure build-up (MPBU) technology tailored for monitoring fracture behavior in geothermal extraction reservoirs. We construct numerical models for geothermal extraction, accounting for dynamic and irregular fractures. Results indicate that the MPBU technology substantially improves the precision of fracture model selection and parameter matching. The interpreted parameters are incorporated into the heat transfer model to assess reservoir heat distribution after 5000 days of extraction. The heat extraction effect of hydraulic fractures is similar to that of irregular fractures. Dynamic fractures have the worst heat extraction effect. Non Sorting Genetic Algorithm II is used to evaluate optimal monitoring and reservoir modification schemes. The innovative technology, numerical models, and optimal solutions will result in an economic improvement of $723,000 per well per year. In conclusion, the integrated monitoring and simulation approach proposed in this paper facilitates efficient and cost-effective differentiation of fracture states, thermal energy extraction evaluation, and optimization of the best extraction strategies to maximize economic benefits for engineers.

An Appearance-Semantic Descriptor with Coarse-to-Fine Matching for Robust VPR.

In recent years, semantic segmentation has made significant progress in visual place recognition (VPR) by using semantic information that is relatively invariant to appearance and viewpoint, demonstrating great potential. However, in some extreme scenarios, there may be semantic occlusion and semantic sparsity, which can lead to confusion when relying solely on semantic information for localization. Therefore, this paper proposes a novel VPR framework that employs a coarse-to-fine image matching strategy, combining semantic and appearance information to improve algorithm performance. First, we construct SemLook global descriptors using semantic contours, which can preliminarily screen images to enhance the accuracy and real-time performance of the algorithm. Based on this, we introduce SemLook local descriptors for fine screening, combining robust appearance information extracted by deep learning with semantic information. These local descriptors can address issues such as semantic overlap and sparsity in urban environments, further improving the accuracy of the algorithm. Through this refined screening process, we can effectively handle the challenges of complex image matching in urban environments and obtain more accurate results. The performance of SemLook descriptors is evaluated on three public datasets (Extended-CMU Season, Robot-Car Seasons v2, and SYNTHIA) and compared with six state-of-the-art VPR algorithms (HOG, CoHOG, AlexNet_VPR, Region VLAD, Patch-NetVLAD, Forest). In the experimental comparison, considering both real-time performance and evaluation metrics, the SemLook descriptors are found to outperform the other six algorithms. Evaluation metrics include the area under the curve (AUC) based on the precision-recall curve, Recall@100%Precision, and Precision@100%Recall. On the Extended-CMU Season dataset, SemLook descriptors achieve a 100% AUC value, and on the SYNTHIA dataset, they achieve a 99% AUC value, demonstrating outstanding performance. The experimental results indicate that introducing global descriptors for initial screening and utilizing local descriptors combining both semantic and appearance information for precise matching can effectively address the issue of location recognition in scenarios with semantic ambiguity or sparsity. This algorithm enhances descriptor performance, making it more accurate and robust in scenes with variations in appearance and viewpoint.

A machine-learning method for biobank-scale genetic prediction of blood group antigens.

A key element for successful blood transfusion is compatibility of the patient and donor red blood cell (RBC) antigens. Precise antigen matching reduces the risk for immunization and other adverse transfusion outcomes. RBC antigens are encoded by specific genes, which allows developing computational methods for determining antigens from genomic data. We describe here a classification method for determining RBC antigens from genotyping array data. Random forest models for 39 RBC antigens in 14 blood group systems and for human platelet antigen (HPA)-1 were trained and tested using genotype and RBC antigen and HPA-1 typing data available for 1,192 blood donors in the Finnish Blood Service Biobank. The algorithm and models were further evaluated using a validation cohort of 111,667 Danish blood donors. In the Finnish test data set, the median (interquartile range [IQR]) balanced accuracy for 39 models was 99.9 (98.9-100)%. We were able to replicate 34 out of 39 Finnish models in the Danish cohort and the median (IQR) balanced accuracy for classifications was 97.1 (90.1-99.4)%. When applying models trained with the Danish cohort, the median (IQR) balanced accuracy for the 40 Danish models in the Danish test data set was 99.3 (95.1-99.8)%. The RBC antigen and HPA-1 prediction models demonstrated high overall accuracies suitable for probabilistic determination of blood groups and HPA-1 at biobank-scale. Furthermore, population-specific training cohort increased the accuracies of the models. This stand-alone and freely available method is applicable for research and screening for antigen-negative blood donors.

Literature Survey for In-Vivo Reynolds and Womersley Numbers of Various Arteries and Implications for Compliant In-Vitro Modelling

PurposeIn-vitro modelling can be used to investigate haemodynamics of arterial geometry and stent implants. However, in-vitro model fidelity relies on precise matching of in-vivo conditions. In pulsatile flow, velocity distribution and wall shear stress depend on compliance, and the Reynolds and Womersley numbers. However, matching such values may lead to unachievable tolerances in phantom fabrication.MethodsPublished Reynolds and Womersley numbers for 14 major arteries in the human body were determined via a literature search. Preference was given to in-vivo publications but in-vitro and in-silico values were presented when in-vivo values were not found. Subsequently ascending aorta and carotid artery case studies were presented to highlight the limitations dynamic matching would apply to phantom fabrication.ResultsSeven studies reported the in-vivo Reynolds and Womersley numbers for the aorta and two for the carotid artery. However, only one study each reported in-vivo numbers for the remaining ten arteries. No in-vivo data could be found for the femoral, superior mesenteric and renal arteries. Thus, information derived in-vitro and in-silico were provided instead. The ascending aorta and carotid artery models required scaling to 1.5× and 3× life-scale, respectively, to achieve dimensional tolerance restrictions. Modelling the ascending aorta with the comparatively high viscosity water/glycerine solution will lead to high pump power demands. However, all the working fluids considered could be dynamically matched with low pump demand for the carotid model.ConclusionThis paper compiles available human haemodynamic information, and highlights the paucity of information for some arteries. It also provides a method for optimal in-vitro experimental configuration.

Identification Dockless Bike-Sharing and Metro Transfer Travelers through Mobility Chain

The burgeoning dockless bike-sharing system presents a promising solution to the first- and last-mile transportation challenge by connecting trip origins/destinations to metro stations. However, the differentiation between metro passengers and DBS riders, as they belong to distinct systems, hinders the precise identification of DBS-metro transfers. This study introduces an innovative method employing mobility chains to establish spatiotemporal relationships, including spatiotemporal conflicts and similarities, among potential users from both systems. This significantly enhances the precision of user matching. An empirical study in Chengdu validates the method’s increased accuracy and examines travel patterns, yielding the following insights: (1) Introduction of the mobility chain reduces average matched pairs by 28.27% and improves accuracy by 18.36%. The addition of spatial-temporal similarity further boosts accuracy by 19.32%. (2) Median distances for DBS-metro access and egress transfers are approximately 950 meters. Short trips of 650–750 meters are prevalent, while trips exceeding 1.5 kilometers lead passengers to opt for alternative modes. (3) Temporal patterns reveal weekday peaks at 8:00, 9:00, and 17:00. On weekends, transfers are uniformly distributed, mainly within urban areas. Suburban stations exhibit reduced weekend activity. These findings can provide valuable insights for enhancing DBS bicycle redistribution, promoting transportation mode integration, and fostering urban transportation’s sustainable development.

The One-Point-One-Line geometry for robust and efficient line segment correspondence

Three-dimensional (3D) lines are common elements in artificial scenes and serve as basic, yet essential features for structural 3D reconstruction. The crucial step of 3D line reconstruction, namely two-view line segment matching, still faces challenges in terms of both accuracy and efficiency improvements. Therefore, robust and efficient constraints are needed to establish valid line candidates. This paper introduces a novel geometry constraint called “one-point-one-line geometry” (OPOL) to enhance the precision of line matching and reduce computational complexity. OPOL offers two remarkable advantages: (1) It takes point orientations as the constraint, which is not only invariant to projective transformations, but also alleviates computational requirements. (2) It needs only one point match to construct the geometry constraint, thus both the grouping and validation are greatly reduced. Additionally, we incorporate the line sweep strategy into OPOL, leveraging depth and space constraints derived from existing 3D points to further enhance efficiency. Extensive experiments conducted on large-coverage and high-resolution images (as large as 10336 × 7788 pixels) demonstrated that OPOL matched lines within a second for an image pair. Both quantitative and qualitative results also demonstrated the superior accuracy and efficiency performance of OPOL. We integrated OPOL into multiple view line reconstruction frameworks, and the promising experimental results reveal the performance of OPOL for robust line reconstruction. The OPOL code is publicly available at https://github.com/JoeAlexxxxx/OPOL.

A stable numerical framework for long-time dynamic crack analysis

This paper introduces a stable numerical framework designed to address dynamic crack problems over long-time intervals. The initial step involves the temporal discretization of the governing dynamic equilibrium equations using the arbitrary order Krylov deferred correction method. To ensure precise boundary condition matching, a novel numerical implementation is incorporated into the Krylov deferred correction technique. Subsequently, the resulting system of spatial partial differential equations at each time node is solved using the meshless generalized finite difference method with 4th-order expansions in regions close to the crack-tips and 2nd-order expansions in areas far from the crack-tips. This combined approach capitalizes on the strengths of both the Krylov deferred correction technique and the generalized finite difference method, enabling stable simulations of dynamic cracks with the large time step and without the need for mesh generation. Notably, we refine the collocation nodes near crack tips to attain accurate numerical results for displacement and stress field. Several numerical experiments involving diverse impact loadings are conducted to validate the developed framework. Furthermore, a comparison is made between the dynamic stress intensity factors obtained using our approach and those from existing methods.

Linking shape conspicuous asymmetry with shape covariation patterns and performance in the insect head and mandibles.

Bilateral symmetry is widespread across animals, yet, among bilaterians, many cases of conspicuous asymmetries evolved. This means that bilaterally homologous structures on the left and right sides display divergent phenotypes. The evolution of such divergent phenotypes between otherwise similarly shaped structures can be thought to be favored by modularity, but this has rarely been studied in the context of left-right differences. Here, we provide an empirical example, using geometric morphometrics to assess patterns of asymmetry and covariation between landmark partitions in a grasshopper with conspicuously asymmetric mandibles. Our morphometric data confirm the presence of strictly directional conspicuous asymmetry in the mandibles and surrounding structures. Covariance patterns and tests hint at a strong integration between mandibles despite their divergent morphologies, and variational modularity with the head capsule. While mandibles have been selected to achieve a key-and-lock morphology by having interlocking shapes, the developmental modularity required to achieve this seems to be overwritten by developmental and/or functional integration, allowing the precise matching required for feeding. The consequent conflicting covariation patterns are reminiscent of the palimpsest model. Finally, the degree of directional asymmetry appears to be under selection, although we find no relationship between bite force and mandible shape or asymmetry.

Synchronizing crowdsourced co-modality between passenger and freight transportation services

This study introduces a synchronized passenger and freight co-modality problem (SPFCP), exploring a crowdsourced co-modality transportation mode where passengers, viewed as an external crowd, volunteer to participate in logistics activities in exchange for compensation. In SPFCP, decision-makers have the option to utilize both crowdsourced passengers (CPs) and self-operated professional couriers (PCs) for deliveries. We formalize the problem and employ a population-based variable neighborhood search to solve it. Our findings indicate that the availability of CPs crucially enhances delivery capacity and efficiency, with the system benefiting from a larger pool of CPs and a higher variability in their travel distances. However, challenges arise from extreme average parcel distances, necessitating precise matching for short distances and extensive crowdsourced resources for long distances. The study underscores the viability of integrating crowdsourced logistics into urban transport systems, suggesting a strategic balance between leveraging crowdsourced solutions and managing logistical complexities.

Manipulating the processing window of directed self-assembly in contact hole shrinking with binary block copolymer/homopolymer blending

Manipulating the processing window of directed self-assembly in contact hole shrinking with binary block copolymer/homopolymer blending

Smart match: revolutionizing organ allocation through artificial intelligence.

In this transformative era of organ transplantation, integrating Smart Match and artificial intelligence (AI) emerges as a pivotal advancement, revolutionizing organ allocation processes. Smart Match employs AI algorithms, enhancing organ matching precision and optimizing transplantation outcomes. Leveraging machine learning addresses complexities in donor-recipient pairing, immunosuppression management, and post-operative care, promising to minimize waitlist mortality and improve patient wellbeing. The multifaceted potential of Smart Match lies in its ability to not only streamline current practices but also pave the way for future innovations in solid organ transplantation. As technology continues to evolve, the collaboration between Smart Match and AI exemplifies a beacon of progress, promising increased efficiency, equitable organ distribution, and improved patient care. This article delves into the paradigm shift facilitated by Smart Match and AI, emphasizing their transformative impact on the landscape of organ allocation and patient outcomes.

Graph-Based Method for Calibration of High-Resolution Mass Spectra of Natural Organic Matter.

Inaccuracies in ion detection and signal processing can undermine confidence in the molecular formula assignment of high-resolution mass spectrometry, which relies on precise matching of the mass-to-charge ratio (m/z). This study proposes a novel graph-based spectra calibration method, MSCMcalib, which implements coordinate transformation and pattern detection. MSCMcalib maps uncalibrated m/z data onto a modified 2D mass defect plot, facilitating the automatic calibration of detected lines, i.e., the calibration of uncalibrated peaks aligned with these lines. The "propagation" method is subsequently employed to accurately and automatically calibrate 605 m/z values across multiple lines, encompassing 98% of the m/z range. The calibrated m/z values divide the m/z range of the spectrum into multiple subintervals, with each subinterval undergoing a process of "scaling" calibration. The utilization of narrower partitions effectively mitigates divergence issues at both ends that arise from the polynomial fitting of errors against m/z. The effectiveness of MSCMcalib is validated through the calibration of SRFA data with m/z error ranges spanning from -10 to -6 ppm, resulting in an additional assignment of 11%-30% more molecular formulas compared to the quadratic fitting calibration.

ALIKE-APPLE: A Lightweight Method for the Detection and Description of Minute and Similar Feature Points in Apples

Current image feature extraction methods fail to adapt to the fine features of apple image texture, resulting in image matching errors and degraded image processing accuracy. A multi-view orthogonal image acquisition system was constructed with apples as the research object. The system consists of four industrial cameras placed around the apple at different angles and one camera placed on top. Following the image acquisition through the system, synthetic image pairs—both before and after transformation—were generated as the input dataset. This generation process involved each image being subjected to random transformations. Through learning to extract more distinctive and descriptive features, the deep learning-based keypoint detection method surpasses traditional techniques by broadening the application range and enhancing detection accuracy. Therefore, a lightweight network called ALIKE-APPLE was proposed for surface feature point detection. The baseline model for ALIKE-APPLE is ALIKE, upon which improvements have been made to the image feature encoder and feature aggregation modules. It comprises an Improved Convolutional Attention Module (ICBAM) and a Boosting Resolution Sampling Module (BRSM). The proposed ICBAM replaced max pooling in the original image feature encoder for downsampling. It enhanced the feature fusion capability of the model by utilizing spatial contextual information and learning region associations in the image. The proposed BRSM replaced the bilinear interpolation in the original feature aggregator for upsampling, overcoming the apple side image’s geometric distortion and effectively preserving the texture details and edge information. The model size was shrunk by optimizing the number of downsampling operations from the image encoder of the original model. The experimental results showed that the average number of observed keypoints and the average matching accuracy were improved by 166.41% and 37.07%, respectively, compared with the baseline model. The feature detection model of ALIKE-APPLE was found to perform better than the optimal SuperPoint. The feature point distribution of ALIKE-APPLE showed an improvement of 10.29% in average standard deviation (Std), 8.62% in average coefficient of variation (CV), and 156.12% in average feature point density (AFPD). Moreover, the mean matching accuracy (MMA) of ALIKE-APPLE improved by 125.97%. Thus, ALIKE-APPLE boasts a more consistent allocation of feature points and greater precision in matching.

Optimizing geothermal reservoir modeling: A unified bayesian PSO and BiGRU approach for precise history matching under uncertainty

This research focuses on optimizing geothermal reservoir modeling tackling issues related to non-uniqueness, subsurface uncertainties, and computational intensity. The proposed workflow integrates Bayesian Particle Swarm Optimization (PSO) and a Bidirectional Gated Recurrent Unit (BiGRU) network to enhance the efficiency of history matching in the context of geothermal resource development under uncertainties. The methodology involves four key steps: firstly, identifying crucial uncertainty parameters such as injection rates and reservoir temperature; secondly, constructing the BiGRU network to capture the nonlinear relationship between these parameters and time series outputs, with Bayesian PSO automating hyper-parameter tuning; thirdly, using Bayesian PSO for the inversion of uncertainty parameters, utilizing the BiGRU as a forward model to reduce computational costs; and finally, conducting a thorough analysis and refinement of errors in predicted responses between the high fidelity model and Bayesian PSO, ensuring the accuracy of the history-matching process. Validation using a 3D fractured geothermal reservoir scenario demonstrates the Bayesian PSO and BiGRU method's efficacy in inverting uncertainty parameters with narrow uncertainties. The Obtained results are compare the performance of predicting the recovery factor (RF) using different surrogate methods, including Gaussian Processes (GP), Radial Basis Function (RBF), and Random Forest Regression (RFR). The proposed Bayesian PSO and GRU-based history-matching approach proves highly accurate and robust for efficiently addressing significant uncertainties in geothermal extraction processes.

MANUAL METHOD FOR GINGIVAL SHADE SELECTION- A LITERATURE REVIEW

Objective- The purpose of the article is to present a narrative review of the literature related to the different shade guides available for dental practitioners. Materials And Methods- A literature search was performed in the online databases – PubMed, Science Direct, Medline and Google Scholar with the following When fabricating a restoration, patient satisfaction with the shade match is essential. The patients level of satisfaction may not be the same as that of the dental practitioner. Esthetic expectations have dramatically increased in the last few years. Color and shade determination are generally considered difficult in dental practice. Most prostheses fail esthetically owing to improper shade selection. A good understanding of color shade guides is essential for precise shade matching, but the paucity of dental practitioners knowledge of color science because of the numerous available shade guides creates a challenge. This comprehensive review states information on shade selection, different shade guides, and shade matching devices commonly used in dental practice. Dental shade guides are a set of colour standards mimicking the colour of tissues, which dentists aim to restore – tooth structures, gingival tissues or facial structures in facial prosthodontics - skin, mucosa, eyes etc. They can be systematized based on the following principles – fabrication process, the material of manufacturing or restoration and according to the tissues, which are restored. Conclusion: To provide an esthetic restoration to the patient, the dentist must have a full understanding of the science of color and color perception. Matching the correct shade satisfies both the dentist and patient and gives a pleasing appearance to the patient.

An Extensive Hubble Space Telescope Study of the Offset and Host Light Distributions of Type I Superluminous Supernovae

We present an extensive Hubble Space Telescope rest-frame UV imaging study of the locations of Type I superluminous supernovae (SLSNe) within their host galaxies. The sample includes 65 SLSNe with detected host galaxies in the redshift range z ≈ 0.05–2. Using precise astrometric matching with SN images, we determine the distributions of the physical and host-normalized offsets relative to the host centers, as well as the fractional flux distribution relative to the underlying UV light distributions. We find that the host-normalized offsets of SLSNe roughly track an exponential disk profile, but exhibit an overabundance of sources with large offsets of 1.5–4 times their hosts' half-light radii. The SLSNe normalized offsets are systematically larger than those of long gamma-ray bursts (LGRBs), and even Type Ib/c and Type II SNe. Furthermore, we find from a Monte Carlo procedure that about 37−8+6% of SLSNe occur in the dimmest regions of their host galaxies, with a median fractional flux value of 0.16, in stark contrast to LGRBs and Type Ib/c and Type II SNe. We do not detect any significant trends in the locations of SLSNe as a function of redshift, or as a function of explosion and magnetar engine parameters inferred from modeling of their optical light curves. The significant difference in SLSN locations compared to LGRBs (and normal core-collapse SNe) suggests that at least some of their progenitors follow a different evolutionary path. We speculate that SLSNe arise from massive runaway stars from disrupted binary systems, with velocities of ∼102 km s−1.