Direct Mapping Research Articles

Comparing Two Methods for Testing the Efficiency of Sports Betting Markets

Sports betting markets can be considered strongly efficient if expected returns on all possible bets on an event are equal. If this form of efficiency holds, then there is a direct mapping from betting odds into probabilities of outcomes of sporting events. We compare two regression-based methods for testing this form of efficiency that have been used in previous research: One that uses normalized probabilities as the explanatory variable for event outcomes and one that uses the inverse of the decimal odds. We show that the normalized probability method produces good tests of the null hypothesis of strong market efficiency but that the inverse odds method does not, with results biased against finding favorite-longshot bias. We illustrate this finding using large datasets of bets and outcomes for tennis and soccer and also with realistic simulations.

Comparative analyses of publicness in urban squares within a diversifying metropolis

AbstractEnsuring publicness and accommodating more diverse human activity in urban public spaces is necessary. This research analyses the relationship between human activities, ownership, and physical features encouraging or discouraging use within the city centre of Dublin (Ireland), a diversifying metropolis experiencing dereliction. Four urban squares were assessed through fieldwork, including direct observation and behavioural mapping. Results indicate that publicness and features encouraging or discouraging use are not directly associated with site ownership. Nor was the presence of nature and landscaping determined by ownership, though it generated microclimates that promoted a higher mix of activities. Implications of going beyond formal definitions of ownership for publicness placed under broader sustainability agendas and recommendations for urban design practice are derived.

Discerning the dynamics of urbanization-climate change-flood risk nexus in densely populated urban mega cities: An appraisal of efficient flood management through spatiotemporal and geostatistical rainfall analysis and hydrodynamic modeling

While the contribution of climate change towards intensifying urban flood risks is well acknowledged, the role of urbanization is less known. The present study, for the first time in flood management literature, explores whether and how unplanned-cum-urbanization may overshadow the contribution of extreme rainfall to flood impacts in densely populated urban regions. To establish this hypothesis and exemplify our proposed framework, the National Capital Territory (NCT) of Delhi in India, infamous for its concurrent flood episodes is selected. The study categorically explores whether the catastrophic 2023 urban flood could have resulted in a similar degree of urban exposure and damage, had it occurred anytime in the past. A comprehensive spatiotemporal and geo-statistical analysis of rainfall over 11 stations brought about through Innovative trend analysis, Omnidirectional and directional Semi-variogram analysis, and Gini Index indicates a rise in extreme rainfalls. High-resolution land-use maps indicate about 39.53 %, 52.66 %, 56.60 %, and 69.18 % of urban footprints during 1993, 2003, 2013, and 2023, while gradient direction maps indicate a prominent urban surge towards the North-West, West, and Southwest corridors. A closer inspection of the Greenness and Urbanity indices reveals a gradual decline in the green footprints and concurrent escalation in the urban footprints over the decades. A 3-way coupled MIKE+ model was set up to replicate the July 2023 flood event; indicating about 13 % of the area experience “high” and “very-high” flood hazards. By overlaying the flood inundation and hazard maps over land-use maps for 1993, 2003, and 2013, we further establish that a similar flood event would have resulted in lesser damage and building exposure. The study offers a set of flood management options for refurbishing resilience and limiting flood risks. The study delivers critical insights into the existing urban flood management strategies while delving into the urban growth-climate change-flood risk nexus.

A data-driven probabilistic evaluation method of hydrogen fuel cell vehicles hosting capacity for integrated hydrogen-electricity network

The growing demand for hydrogen in hydrogen fuel cell vehicles (HFCVs) will present challenges for the safe operation of the integrated distributed hydrogen supply network (DHSN) and power distribution network (PDN). This paper proposes a data-driven probabilistic evaluation method for determining the hosting capacity of hydrogen fuel cell vehicles (HVHC). Firstly, a directional mapping method is proposed to model the maximum safety boundaries of critical electrolyzers. It integrates the thermal-electrical and dynamic operating characteristics while maintaining the full boundaries in dimension reduction to ensure accuracy and efficiency. A probabilistic HVHC evaluation model is developed to consider uncertain factors of high dimensions despite data deficiency, such as HFCV refueling demand and renewable power. The proposed model determines the maximum network tolerance for the HFCV number, constrained by the safety constraints of the PDN integrated with DHSN, on-site and off-site hydrogen supplies coupled by tube trailers. Finally, a cross-term decoupled data-driven polynomial chaos expansion is proposed to efficiently solve the developed probabilistic HVHC model. It is established based on raw and small samples without extracted probability distribution information. The solution approach also combines the cross-terms in expansions using Taylor expansion, making it efficient for high-dimensional problems. Furthermore, the accuracy and scale reduction effect of the solution algorithm are proven based on Wasserstein ambiguity sets. Numerical studies on three systems show that the proposed method has only a 0.01% error in HVHC results and an 8.38% computation time of the Monte Carlo method.

CSIP: Contrastive learning with Graph Neural Network enables chemical structure to image mapping

The development of advanced AI has been transforming the biomedical field. The emergence of multi-modal biomedical data such as imaging, sequencing, and other omics data further enabled the training of AI models for complex analytical tasks. However, such data analysis remains challenging due to the difficulty of integrating information from different modalities. In this paper, we aim to address this challenge by proposing a methodology to integrate image and molecular structure data. We present the Contrastive Structure-to-Image Pretraining (CSIP) framework, which leverages a self-supervised Graph Neural Network (GNN) to encode molecules and images into a joint feature embedding space. This direct mapping between the two modalities enables a wide variety of applications, including image profiling and clustering of molecules based on their effects on cell morphology. Image profiles generated by CSIP archived an average AUC of 0.708 on various biological activity prediction tasks, rivalling the state-of-the-arts and outperforming some fully supervised methodologies. Further, CSIP improved the accuracy of image-molecule matching by 29-folds from the random baseline after being trained on a small dataset, which demonstrated data efficiency. The code to reproduce our results can be found at https://github.com/LeoL18/CSIP.

Deep learning solutions for inverse problems in advanced biomedical image analysis on disease detection

Inverse problems in biomedical image analysis represent a significant frontier in disease detection, leveraging computational methodologies and mathematical modelling to unravel complex data embedded within medical images. These problems include deducing the unknown properties of biological structures or tissues from the observed imaging data, presenting a unique challenge in decoding intricate biological phenomena. Regarding disease detection, this technique has played a critical role in optimizing diagnostic efficiency by extracting meaningful insights from different imaging modalities like molecular imaging, MRI, and CT scans. Inverse problems contribute to uncovering subtle abnormalities by employing iterative optimization techniques and sophisticated algorithms, enabling precise and early disease detection. Deep learning (DL) solutions have emerged as robust mechanisms for addressing inverse problems in biomedical image analysis, especially in disease recognition. Inverse problems involve reconstructing unknown structures or parameters from observed data, and the DL model excels in learning complex representations and mappings. This study develops a DL Solution for Inverse Problems in the Advanced Biomedical Image Analysis on Disease Detection (DLSIP-ABIADD) technique. The DLSIP-ABIADD technique exploits the DL approach to solve inverse problems and detect the presence of diseases on biomedical images. To solve the inverse problem, the DLSIP-ABIADD technique uses a direct mapping approach. Bilateral filtering (BF) is used for image preprocessing. Besides, the MobileNetv2 model derives feature vectors from the input images. Moreover, the Henry gas solubility optimization (HGSO) method is applied for optimal hyperparameter selection of the MobileNetv2 model. Furthermore, a bidirectional long short-term memory (BiLSTM) model is deployed to identify diseases in medical images. Extensive simulations have been involved to illustrate the better performance of the DLSIP-ABIADD technique. The experimentation outcomes stated that the DLSIP-ABIADD technique performs better than other models.

Lateralization of acquisition and consolidation in direction but not amplitude of a motor skill task.

Previous research suggests that the neural processes underlying specification of movement direction and amplitude are independently represented in the nervous system. However, our understanding of acquisition and consolidation processes in the direction and distance learning remains limited. We designed a virtual air hockey task, in which the puck direction is determined by the hand direction at impact, while the puck distance is determined by the amplitude of the velocity. In two versions of this task, participants were required to either specify the direction or the distance of the puck, while the alternate variable did not contribute to task success. Separate groups of right-handed participants were recruited for each task. Each participant was randomly assigned to one of two groups with a counter-balanced arm practice sequence (right to left, or left to right). We examined acquisition and, after 24h, we examined two aspects of consolidation: 1) same hand performance to test the durability and 2)the opposite hand to test the effector-independent consolidation (interlimb transfer) of learning. The distance task showed symmetry between hands in the extent of acquisition as well as in both aspects of consolidation. In contrast, the direction task showed asymmetry in both acquisition and consolidation: the dominant right arm showed faster and greater acquisition and greater transfer from the opposite arm training. The asymmetric acquisition and consolidation processes shown in the direction task might be explained by lateralized control and mapping of direction, an interpretation consistent with previous findings on motor adaptation paradigms.

Patient safety reporting and learning system of Catalonia (SNiSP Cat): a health policy initiative to enhance culture, leadership and professional engagement

Patient safety reporting and learning systems (PSRLS) are tools to promote patient safety culture in healthcare organisations (HCO). Many PRSLS are locally developed. WHO Global Action Plan on Patient Safety...

Developing mapping algorithms to predict EQ-5D health utility values from Bath Ankylosing Spondylitis Disease Activity Index and Bath Ankylosing Spondylitis Functional Index among patients with Ankylosing Spondylitis

BackgroundPreference-based measures of health-related quality of life (HRQoL), such as the EQ-5D or the SF-6D, are essential for health economic evaluation. However, they are rarely included in clinical trials of ankylosing spondylitis (AS). This study aims to develop mapping algorithms to predict EQ-5D-3L and EQ-5D-5L health utility scores from the Bath Ankylosing Disease Activity Index (BASDAI) and the Bath Ankylosing Spondylitis Functional Index (BASFI).MethodsPatients with AS were recruited from the largest tertiary hospital in Shandong province, China, between December 2019 and October 2020. Patients were selected by convenience sampling method according to the following criteria: (1) diagnosed with AS according to the New York criteria; (2) aged 18 years and above; and (3) without mental disorders; (4) able to understand the questionnaires; (5) without serious complications. There were 243 patients who completed the face-to-face questionnaire survey, and 5 cases with missing values in key variables were excluded. Ordinary least squares, censored least absolute deviations, Tobit, adjusted limited dependent variable mixture model and beta-mixture model (BM) in the direct approach and ordered logit and multinomial logit (Mlogit) model in the response approach were used to develop mapping algorithms. Mean absolute error, root mean square error, Spearman’s correlation coefficient and concordance correlation coefficient were used to access predictive performance.ResultsThe 238 patients with AS had a mean age of 35.19 (SD = 9.59) years, and the majority (74.47%) were male. The observed EQ-5D-3L and EQ-5D-5L health utility values were 0.88 (SD = 0.12) and 0.74 (SD = 0.27), respectively. The EQ-5D-5L had higher conceptual overlap with the BASDAI and BASFI than the EQ-5D-3L did. The Mlogit was the best-performing model for the EQ-5D-3L, and the BM showed better performance in predicting EQ-5D-5L than other direct and indirect mapping models did.ConclusionThis study demonstrates that the EQ-5D-5L, rather than EQ-5D-3L, should be selected as the target outcome measure of HRQoL in patients with AS in China, and the BM mapping algorithm could be used to predict EQ-5D-5L values from BASDAI and BASFI for health economic evaluation.

Direct Optimal Mapping Image Power Spectrum and its Window Functions

The key to detecting neutral hydrogen during the epoch of reionization (EoR) is to separate the cosmological signal from the dominating foreground radiation. We developed direct optimal mapping (DOM) to map interferometric visibilities; it contains only linear operations, with full knowledge of point spread functions from visibilities to images. Here, we demonstrate a fast Fourier transform-based image power spectrum and its window functions computed from the DOM images. We use noiseless simulation, based on the Hydrogen Epoch of Reionization Array Phase I configuration, to study the image power spectrum properties. The window functions show <10−11 of the integrated power leaks from the foreground-dominated region into the EoR window; the 2D and 1D power spectra also verify the separation between the foregrounds and the EoR.

Direct and Indirect Mapping of Assessment of Quality of Life - 6 Dimensions (AQoL-6D) Onto EQ-5D-5L Utilities Using Data From a Multicenter, Cross-Sectional Study of Malaysians With Chronic Heart Failure

ObjectivesThe Assessment of Quality of Life - 6 Dimensions (AQoL-6D), a generic preference-based measure, is an appealing alternative to EQ-5D-5L for assessing health status in patients with chronic heart failure (HF), given its expanded scope. However, without a Malaysian value set, the AQoL-6D cannot generate health state utility values (HSUVs) to support local economic evaluations. This study intended to develop algorithms for predicting EQ-5D-5L HSUVs from AQoL-6D in an HF population. MethodsCross-sectional data from a multicenter cohort of 419 HF outpatients were used. Both direct and indirect mapping approaches were attempted using 5 sets of explanatory variables and 8 models (ordinary least squares, Tobit, censored least absolute deviations, generalized linear model, 2-part model [TPM], beta regression-based model, adjusted limited dependent variable mixture model, and multinomial ordinal regression [MLOGIT]). The models’ predictive performance was assessed through 10-fold cross-validated mean absolute error [MAE] and root mean squared error [RMSE]). Potential prediction bias was also examined graphically. The best-performing models, with the lowest RMSE and no bias, were then identified. ResultsAmong the models evaluated, TPM, which included age, sex, and 5 AQoL-6D dimension scores as predictors, appears to be the best-performing model for directly predicting EQ-5D-5L HSUVs from AQoL-6D. TPM yielded the lowest MAE (0.0802) and RMSE (0.1116), and demonstrated predictive accuracy for HSUVs >0.2 without significant bias. A MLOGIT model developed for response mapping had suboptimal predictive accuracy. ConclusionsThis study developed potentially useful mapping algorithms for generating Malaysian EQ-5D-5L HSUVs from AQoL-6D responses among patients with HF when direct EQ-5D-5L data are unavailable.

Glacier dynamic study of the Terre de Lambert glacier in Greenland based on Landsat 8 and Sentinel-1 data

With global temperatures on the rise, the trend of glacial melting is becoming increasingly evident. This study focuses on the northeastern region of Greenland as the research subject, utilizing remote sensing technology and geographic information technology to study glacial dynamics. Initially, the research processes Landsat 8 data using QGIS to classify the widespread snow and ice cover, facilitating the observation and analysis of its temporal variations. The study then narrows its focus to Terre de Lambert for further analysis. Subsequently, using satellite data from Sentinel-1 and employing SNAP, the research conducts an in-depth analysis of glacier flow speed in specific regions. Through intricate computational models and algorithms, precise glacier flow direction and speed maps are generated. By combining historical data, the study compares and analyzes the glacier flow speed between 2018 and from June 20th to July 14th, 2023. Results indicate that the glacier flow speed in 2023 during the same time frame was faster. Throughout the research process, limitations in current data acquisition and processing methods were identified. Thus, the paper suggests improvements to data acquisition and processing tools to enhance the efficiency of glacier flow speed studies. Theres a fervent hope for the emergence of more convenient, streamlined, and customizable temporal dimension tools for glacier flow speed data in the future. The research findings aim to draw global citizens attention to the issue of glacial melting and inspire collective actions to protect our planet.

Particle tracking, recognition and LET evaluation of out-of-field proton therapy delivered to a phantom with implants

Objective. This study aims to assess the composition of scattered particles generated in proton therapy for tumors situated proximal to some titanium (Ti) dental implants. The investigation involves decomposing the mixed field and recording Linear Energy Transfer (LET) spectra to quantify the influence of metallic dental inserts located behind the tumor. Approach. A therapeutic conformal proton beam was used to deliver the treatment plan to an anthropomorphic head phantom with two types of implants inserted in the target volume (made of Ti and plastic, respectively). The scattered radiation resulted during the irradiation was detected by a hybrid semiconductor pixel detector MiniPIX Timepix3 that was placed distal to the Spread-out Bragg peak. Visualization and field decomposition of stray radiation were generated using algorithms trained in particle recognition based on artificial intelligence neural networks (AI NN). Spectral sensitive aspects of the scattered radiation were collected using two angular positions of the detector relative to the beam direction: 0° and 60°. Results. Using AI NN, 3 classes of particles were identified: protons, electrons & photons, and ions & fast neutrons. Placing a Ti implant in the beam’s path resulted in predominantly electrons and photons, contributing 52.2% of the total number of detected particles, whereas for plastic implants, the contribution was 65.4%. Scattered protons comprised 45.5% and 31.9% with and without metal inserts, respectively. The LET spectra were derived for each group of particles identified, with values ranging from 0.01 to 7.5 keV μm−1 for Ti implants/plastic implants. The low-LET component was primarily composed of electrons and photons, while the high-LET component corresponded to protons and ions. Significance. This method, complemented by directional maps, holds the potential for evaluating and validating treatment plans involving stray radiation near organs at risk, offering precise discrimination of the mixed field, and enhancing in this way the LET calculation.

Determination of Surface Charge Density and Charge Mapping of CYTOP Film in Air using Electrostatic Force Microscopy.

Cyclic transparent optical polymer (CYTOP), a fluoropolymer, finds a plethora of applications in microelectronic devices for sustainable energy harvesting and memory devices. By and large, these devices demand high voltage breakdown, a high dielectric constant, transparency, charge storage, and retention capabilities. Despite many efforts, comprehensive investigation of the charge distribution, retention, and discharge studies conducted on the CYTOP film at the micro-scale remains elusive. Here, we present direct quantification and mapping of surface charge on the CYTOP surface at room temperature using two different modes of advanced surface probe microscopy i.e., Kelvin probe force microscopy (KPFM) and electrostatic force microscopy (EFM). We estimated that the surface charge densities of the CYTOP film using EFM are 1.4 and 3.3 μC/cm2 for the injection of positive and negative charges, respectively. Furthermore, we determined the charge retention time for both injected positive and negative charges. We found that the retention capacity of the negative charges on the CYTOP film is much higher as compared to the positive charges. Moreover, it is also observed that injected negative charges are strongly localized on the CYTOP surface compared to the positive counterpart. Additionally, we demonstrated that charge writing is possible on the CYTOP surface using the AFM conductive tip. These results may find potential applications in energy harvesting, sensing, memory devices, security, and surveillance.

Assessment of Regional Development Using the Unit Genetic Area (GSW) Method in the Mining Environment Area of Palu City, Central Sulawesi Province

Introduction: The growth of the mining industry in Central Sulawesi is currently growing rapidly, starting from excavations A, and B to C. The Buluri area currently has many active C excavation mines, but does the area have the potential for mining area development? This research was conducted with the aim of knowing the level of potential or constraints on the development of mining areas. Method: The location of this research is located in Buluri Village, Ulujadi District, Palu City, Central Sulawesi. The method used in this research is direct mapping in the field to determine the genetic unit of the region and scoring on each subfactor of the genetic unit of the region to determine the development of mining areas. Results and Discussion: The results showed that in the Buluri area, there are two regional genetic units, namely the genetic unit of the moderately deformed porphyry andesite plain area (1221) and the genetic unit of the moderately deformed porphyry andesite hills area (1222). Conclusion: Based on the results of subfactor calculations in each regional genetic unit, it is found that the genetic unit of the moderately deformed porphyry andesite plain area (1221) is included in the low-level mining area development category, and the genetic unit of the moderately deformed porphyry andesite hills area (1222) is included in the medium- level mining area development category.

Inverse mapping of quantum properties to structures for chemical space of small organic molecules

Computer-driven molecular design combines the principles of chemistry, physics, and artificial intelligence to identify chemical compounds with tailored properties. While quantum-mechanical (QM) methods, coupled with machine learning, already offer a direct mapping from 3D molecular structures to their properties, effective methodologies for the inverse mapping in chemical space remain elusive. We address this challenge by demonstrating the possibility of parametrizing a chemical space with a finite set of QM properties. Our proof-of-concept implementation achieves an approximate property-to-structure mapping, the QIM model (which stands for “Quantum Inverse Mapping”), by forcing a variational auto-encoder with a property encoder to obtain a common internal representation for both structures and properties. After validating this mapping for small drug-like molecules, we illustrate its capabilities with an explainability study as well as by the generation of de novo molecular structures with targeted properties and transition pathways between conformational isomers. Our findings thus provide a proof-of-principle demonstration aiming to enable the inverse property-to-structure design in diverse chemical spaces.

Curvature programming of freestanding 3D mesostructures and flexible electronics based on bilayer ribbon networks

Three-dimensional (3D) buckling assembly of flexible electronics from strategically designed two-dimensional (2D) precursor structures has enabled important applications in a variety of areas, owing to its versatile applicability to a broad range of length scales and high-performance materials, as well as to a rich diversity of 3D topologies. Rational design methods that allow direct mapping of 3D mesostructures onto unknown 2D precursor structures and loading parameters are foundational to these assembly technologies, but face scientific challenges, such as the high nonlinearity of spatial deformations and tricky bifurcation behaviors. While a few inverse design methods based on the beam theory, topology optimization and machine learning algorithms have been reported, the shape programming of freestanding 3D mesostructures/electronics with highly complex curvature distributions remains elusive. In this work, we propose a curvature programming method based on bilayer ribbon networks, along with a mold-assisted assembly strategy, as a new route to customizable freestanding 3D mesostructures and electronics. Combined mechanics modeling, finite element analyses and experimental measurements allow a clear understanding of nonlinear bending-stretching coupled deformations of bilayer ribbon networks during the 2D-to-3D transformation. A parameter domain with one-to-one mapping of the dimensionless curvature and the bending stiffness ratio is identified, offering a theoretical basis of the curvature programming. By introducing a discretization strategy, a variety of regular (e.g., circles, ellipses, spirals and toroids) and biomimetic 3D curved ribbons and mesosurfaces (e.g., mimicking wavy vines, diatoms and arbitrarily curled leaves) were inversely designed and experimentally realized. A device demonstration capable of strain/temperature sensing and micro-LEDs indication suggests application opportunities in bioelectronics and microelectromechanical systems.

An adaptive lumped-mass dynamic model and its control application for continuum robots

Dynamic modeling for continuum robots remains challenging due to their large nonlinear deformation and the variation of dynamic parameters during movement. In this paper, a lumped-mass dynamic model (LMD) for a continuum robot is constructed including elastic and viscous parameters in the robotic joints. Then the appropriate dynamic parameters (e.g. spring and damping coefficients of the LMD) with respect to the motion status (e.g. position and velocity of the robot) are estimated using a Genetic Algorithm (GA). Based on the obtained data set, a Multi-Layer Perception (MLP) is trained to establish a direct mapping from the motion status to the dynamic parameters, so the LMD can tune its parameters in real-time when moving within the workspace, resulting an adaptive lumped-mass dynamic model (ALMD). Compared to the fixed-parameter LMD, the modeling error of the ALMD is reduced by up to 60.2 %. Finally, a feedforward controller is implemented to control a continuum robotic prototype using the presented ALMD, reducing the maximum tracking error by 67.5 %.

Multimodal Image Fusion Workflow Incorporating MALDI Imaging Mass Spectrometry and Microscopy for the Study of Small Pharmaceutical Compounds.

Multimodal imaging analyses of dosed tissue samples can provide more comprehensive insights into the effects of a therapeutically active compound on a target tissue compared to single-modal imaging. For example, simultaneous spatial mapping of pharmaceutical compounds and endogenous macromolecule receptors is difficult to achieve in a single imaging experiment. Herein, we present a multimodal workflow combining imaging mass spectrometry with immunohistochemistry (IHC) fluorescence imaging and brightfield microscopy imaging. Imaging mass spectrometry enables direct mapping of pharmaceutical compounds and metabolites, IHC fluorescence imaging can visualize large proteins, and brightfield microscopy imaging provides tissue morphology information. Single-cell resolution images are generally difficult to acquire using imaging mass spectrometry but are readily acquired with IHC fluorescence and brightfield microscopy imaging. Spatial sharpening of mass spectrometry images would thus allow for higher fidelity coregistration with other higher-resolution microscopy images. Imaging mass spectrometry spatial resolution can be predicted to a finer value via a computational image fusion workflow, which models the relationship between the intensity values in the mass spectrometry image and the features of a high-spatial resolution microscopy image. As a proof of concept, our multimodal workflow was applied to brain tissue extracted from a Sprague-Dawley rat dosed with a kratom alkaloid, corynantheidine. Four candidate mathematical models, including linear regression, partial least-squares regression, random forest regression, and two-dimensional convolutional neural network (2-D CNN), were tested. The random forest and 2-D CNN models most accurately predicted the intensity values at each pixel as well as the overall patterns of the mass spectrometry images, while also providing the best spatial resolution enhancements. Herein, image fusion enabled predicted mass spectrometry images of corynantheidine, GABA, and glutamine to approximately 2.5 μm spatial resolutions, a significant improvement compared to the original images acquired at 25 μm spatial resolution. The predicted mass spectrometry images were then coregistered with an H&E image and IHC fluorescence image of the μ-opioid receptor to assess colocalization of corynantheidine with brain cells. Our study also provides insights into the different evaluation parameters to consider when utilizing image fusion for biological applications.

Crosstalk between epitranscriptomic and epigenomic modifications and its implication in human diseases

Crosstalk between N6-methyladenosine (m6A) and epigenomes is crucial for gene regulation, but its regulatory directionality and disease significance remain unclear. Here, we utilize quantitative trait loci (QTLs) as genetic instruments to delineate directional maps of crosstalk between m6A and two epigenomic traits, DNA methylation (DNAme) and H3K27ac. We identify 47 m6A-to-H3K27ac and 4,733 m6A-to-DNAme and, in the reverse direction, 106 H3K27ac-to-m6A and 61,775 DNAme-to-m6A regulatory loci, with differential genomic location preference observed for different regulatory directions. Integrating these maps with complex diseases, we prioritize 20 genome-wide association study (GWAS) loci for neuroticism, depression, and narcolepsy in brain; 1,767 variants for asthma and expiratory flow traits in lung; and 249 for coronary artery disease, blood pressure, and pulse rate in muscle. This study establishes disease regulatory paths, such as rs3768410-DNAme-m6A-asthma and rs56104944-m6A-DNAme-hypertension, uncovering locus-specific crosstalk between m6A and epigenomic layers and offering insights into regulatory circuits underlying human diseases.