Precise Matching Research Articles

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.

Utilizing Gate-Controlled Supercurrent for All-Metallic Tunable Superconducting Microwave Resonators.

Hybridizing a microwave mode with a quantum state requires precise frequency matching of a superconducting microwave resonator and the corresponding quantum object. However, fabrication always brings imperfections in geometry and material properties, causing deviations from the desired operating frequencies. An effective and universal strategy for their resonant coupling is to tune the frequency of a resonator, as quantum states like phonons are hardly tunable. Here, we demonstrate gate-tunable, titanium-nitride (TiN)-based superconducting resonators by implementing a nanowire inductor whose kinetic inductance is tuned via the gate-controlled supercurrent (GCS) effect. We investigate their responses for different gate biases and observe 4% (∼150 MHz) frequency tuning with decreasing internal quality factors. We also perform temperature-controlled experiments to support phonon-related mechanisms in the GCS effect and the resonance tuning. The GCS effect-based method proposed in this study provides an effective route for locally tunable resonators that can be employed in various hybrid quantum devices.

Matching cost function analysis and disparity optimization for low-quality binocular images

State-of-the-art dense stereo matching algorithms have achieved excellent performance, demonstrating a capability to attain precise matching in most areas. However, current such methods rarely achieve this when images are captured under poor conditions. To improve the accuracy of the algorithm in such cases, this paper introduces a post-optimization algorithm to rectify matching errors and enhance outcomes. The main research areas of this paper include three aspects. (1) Disparities are classified into reliable and unreliable results based on the analysis of geometric matching relationships, local features in the images, and components within the matching cost function; (2) Subsequent analysis of horizontal image features identifies local characteristic indices calculated through integration along the horizontal axis, which establish specific matching criteria, forming the foundation for a cost volume that encompasses these distinct matches; (3) A redefined matching cost function is applied to previously classified unreliable results to rectify matching errors. This energy function is based on the cost volume above. Experimental results validate the efficacy of the proposed post-optimization algorithm, reducing the average matching errors from 8.66% to 5.85%.

Electro-metabolic signaling.

Precise matching of energy substrate delivery to local metabolic needs is essential for the health and function of all tissues. Here, we outline a mechanistic framework for understanding this critical process, which we refer to as electro-metabolic signaling (EMS). All tissues exhibit changes in metabolism over varying spatiotemporal scales and have widely varying energetic needs and reserves. We propose that across tissues, common signatures of elevated metabolism or increases in energy substrate usage that exceed key local thresholds rapidly engage mechanisms that generate hyperpolarizing electrical signals in capillaries that then relax contractile elements throughout the vasculature to quickly adjust blood flow to meet changing needs. The attendant increase in energy substrate delivery serves to meet local metabolic requirements and thus avoids a mismatch in supply and demand and prevents metabolic stress. We discuss in detail key examples of EMS that our laboratories have discovered in the brain and the heart, and we outline potential further EMS mechanisms operating in tissues such as skeletal muscle, pancreas, and kidney. We suggest that the energy imbalance evoked by EMS uncoupling may be central to cellular dysfunction from which the hallmarks of aging and metabolic diseases emerge and may lead to generalized organ failure states-such as diverse flavors of heart failure and dementia. Understanding and manipulating EMS may be key to preventing or reversing these dysfunctions.

Appearance flow estimation for online virtual clothing warping via optimal feature linear assignment

Clothing warping to spatially align source garments with the corresponding body parts is crucial in clothing media tasks such as virtual try-on and pose-guided person generation. Recent pioneering work has utilized flow fields with additional dimensions of freedom to simulate clothing warping flexibly. However, current methods for estimating appearance flow typically rely on calculating just the local cost volume which contains multiple noisy matching points, potentially leading to a mismatch between clothing and body parts. To address this issue, we propose a novel appearance flow estimation network(Warping-Flow) for clothing warping based on optimal features linear assignment. Specially, two remarkable contributions are made to improve feature matching precision. First, a local context feature aggregation module is proposed to enhance the semantic feature distinction of the source cloth and target pose. Second, Warping-Flow estimates a hard attention mask through the cost volume to filter irrelevant features, followed by the optimal linear assignment algorithm to normalize the cost volume to a discrete permutation matrix that explicitly models the most contributing bipartite matches. Experiments conducted on the VITON and VITON-HD datasets demonstrate that Warping-Flow outperforms existing state-of-the-art algorithms, particularly in cases involving complex clothing deformation. Furthermore, Warping-Flow can serve as a plug-in to improve existing garment media technologies.

A photothermal MoS2 decorated biomass carbon-based aerogel with a directionally aligned porous structure for mitigating heavy metal stress under seawater acidification.

Marine animals and human are threatened by seawater acidification and metal contamination. Especially, the toxicity of copper (Cu) is expected to be boosted with seawater acidification. However, studies on the removal of Cu under seawater acidification are limited for practical applications, owing to obstacles such as instability, secondary contamination, and low adsorption efficiency. In this work, coconut shells were utilized for the synthesis of biomass carbon, which was then decorated with MoS2. A novel porous MoS2/carbon-based aerogel (MCA) with the synergistic effect of photothermal conversion and adsorption was constructed via directional freeze-drying technology. The adsorption properties of MCA were a precise match with Freundlich isotherm and pseudo-second-order kinetic models with a high correlation coefficient (R2) of more than 0.995. Under solar illumination, the surface temperature of MCA reached up to 36.3 °C and the adsorption capacity of MCA increased to 833.8 mg g-1, indicating that the remarkable thermal properties of MCA contributed to achieving high adsorption capacity. The adsorption mechanisms of MCA involved in the removal of Cu(ii) ions were dominated by chemisorption rather than surface physical adsorption. Owing to its outstanding photothermal conversion performance and directionally aligned porous structure, MCA was able to remove Cu(ii) species from seawater, and the adsorption ability of MCA reached 247.1 mg g-1 after ten adsorption cycles. MCA exhibited excellent stability to resist the complex natural environment and was easy to reuse. Overall, MCA with a series of merits, including high adsorption efficiency, excellent photothermal conversion property, and outstanding cycling stability, was confirmed to contribute to addressing heavy metal stress under seawater acidification.

Low-Level Radio-Frequency system integrated with feed-forward control and vector modulation

To provide a more accurate and stable Radio-Frequency (RF) signal in conditioning and processing test progress, it is necessary to design a Low-Level Radio-Frequency (LLRF) control system which can provide high precision RF driving signal based on meeting the amplitude and phase stabilization requirement. Through Feed-Forward operation, accurate phase adjustment and amplitude adjustment are realized inside the pulse, to realize the precision and automation of phase-inversion, amplitude stabilization, phase stabilization, and wave-form adaptation matching. An LLRF System integrated with feed-forward control and vector modulation output was designed and built, and the long-term working stability of the LLRF system was verified during a new 50MW S-band Klystron conditioning progress.