Intrinsic Limitations Research Articles

The Evidence Contraction Issue in Deep Evidential Regression: Discussion and Solution

Deep Evidential Regression (DER) places a prior on the original Gaussian likelihood and treats learning as an evidence acquisition process to quantify uncertainty. For the validity of the evidence theory, DER requires specialized activation functions to ensure that the prior parameters remain non-negative. However, such constraints will trigger evidence contraction, causing sub-optimal performance. In this paper, we analyse DER theoretically, revealing the intrinsic limitations for sub-optimal performance: the non-negativity constraints on the Normal Inverse-Gamma (NIG) prior parameter trigger the evidence contraction under the specialized activation function, which hinders the optimization of DER performance. On this basis, we design a Non-saturating Uncertainty Regularization term, which effectively ensures that the performance is further optimized in the right direction. Experiments on real-world datasets show that our proposed approach improves the performance of DER while maintaining the ability to quantify uncertainty.

Halide Superionic Conductors with Non‐Close‐Packed Anion Frameworks

AbstractHalide superionic conductors (SICs) are drawing significant research attention for their potential applications in all‐solid‐state batteries. A key challenge in developing such SICs is to explore and design halide structural frameworks that enable rapid ion movement. In this work, we show that the close‐packed anion frameworks shared by traditional halide ionic conductors face intrinsic limitations in fast ion conduction, regardless of structural regulation. Beyond the close‐packed anion frameworks, we identify that the non‐close‐packed anion frameworks have great potential to achieve superionic conductivity. Notably, we unravel that the non‐close‐packed UCl3‐type framework exhibit superionic conductivity for a diverse range of carrier ions, including Li+, Na+, K+, and Ag+, which are validated through both ab initio molecular dynamics simulations and experimental measurements. We elucidate that the remarkable ionic conductivity observed in the UCl3‐type framework structure stems from its significantly more distorted site and larger diffusion channel than its close‐packed counterparts. By employing the non‐close‐packed anion framework as the key feature for high‐throughput computational screening, we also identify LiGaCl3 as a promising candidate for halide SICs. These discoveries provide crucial insights for the exploration and design of novel halide SICs.

Halide Superionic Conductors with Non-Close-Packed Anion Frameworks.

Halide superionic conductors (SICs) are drawing significant research attention for their potential applications in all-solid-state batteries. A key challenge in developing such SICs is to explore and design halide structural frameworks that enable rapid ion movement. In this work, we show that the close-packed anion frameworks shared by traditional halide ionic conductors face intrinsic limitations in fast ion conduction, regardless of structural regulation. Beyond the close-packed anion frameworks, we identify that the non-close-packed anion frameworks have great potential to achieve superionic conductivity. Notably, we unravel that the non-close-packed UCl3 -type framework exhibit superionic conductivity for a diverse range of carrier ions, including Li+ , Na+ , K+ , and Ag+ , which are validated through both ab initio molecular dynamics simulations and experimental measurements. We elucidate that the remarkable ionic conductivity observed in the UCl3 -type framework structure stems from its significantly more distorted site and larger diffusion channel than its close-packed counterparts. By employing the non-close-packed anion framework as the key feature for high-throughput computational screening, we also identify LiGaCl3 as a promising candidate for halide SICs. These discoveries provide crucial insights for the exploration and design of novel halide SICs.

Enhancing Part Quality Management Using a Holistic Data Fusion Framework in Metal Powder Bed Fusion Additive Manufacturing

Abstract Metal powder bed fusion additive manufacturing (AM) processes have gained widespread adoption for the ability to produce complex geometries with high performance. However, a multitude of factors still affect the build process, which significantly impacts the adoption rate. This, in turn, leads to great challenges in achieving consistent and reliable part quality. To address this challenge, simulations and measurements have been progressively deployed to provide valuable insights into the quality of individual builds. This paper proposes an AM data fusion framework that combines data sources beyond a single-part, development cycle. Those sources include the aggregation of measurements from multiple builds and the outputs from their related models and simulations. Both can be used to support decision-makings that can improve part quality. The effectiveness of the holistic AM data fusion framework is illustrated through three use case scenarios: one that fuses process data from a single build, one that fusses data from a build and simulation, and one that fuses data from multiple builds. The case studies demonstrate that a data fusion framework can be applied to effectively detect over-melting scan strategies, monitor material melting conditions, and predict down-skin surface defects. Overall, the proposed method provides a practical solution for enhancing part quality management when individual data sources or models have intrinsic limitations.

Development and Characterizations of Fine Pitch Flip-Chip Interconnection Using Silver Sintering

Flip-chip interconnects made of silver are promising candidates to overcome the intrinsic limits of solderbased interconnects and match the demand for increased current densities of high-performance microprocessors. Dipbased interconnects have been demonstrated to be a promising approach to form electrical interconnects by sintering paste between copper pillars and pads. However, the quality of the process is limited by residual porosity and poor performances of the sintered joint formed between the pillar and the pad during sintering if a pressure > 50 MPa is not applied in order to decrease the final porosity. In this study, development has been focused on varying key dipping process parameters allowing a pressureless sintering process. Dip-transfer process was optimized on test vehicle and has shown electrical continuity over 700 interconnections with diameter down to 50 μm. We demonstrate high reliability of the process with microstructural observations, tomography X and thermal cycle up to 200 cycles without breakdown.

(141) Can a Nocturnal Penile Tumescence and Rigidity Test Detect Organic Aetiology of Erectile Dysfunction? A Peripheral and Penile Vascular Study

Abstract Introduction In the diagnostic workup of erectile dysfunction (ED) the Nocturnal Penile Tumescence and Rigidity test (NPTR) is being used less and less because of its inconvenience but also in part because of the intrinsic limits of this test. In particular, the NPTR has been considered very useful to diagnose psychogenic ED when it is positive (normal) but less informative when it is negative (pathological) because of the high incidence of false negatives. Other tests have been suggested to better diagnose an organic ED, especially the dynamic Penile Colour-Doppler Ultrasound (PCDU) to study the penile vascular response and detect a vasculogenic ED. Objective The purpose of the study was to explore the value of a pathological NPTR by investigating relationship between the NPTR result and the peripheral vascular health at the carotid, brachial and cavernous arteries. Methods We conducted an observational study involving patients with ED who had undergone a NPTR as part of their ED diagnostic workup. We assessed their peripheral vascular health through (1) the measurement of the intima-media thickness at the carotid arteries (cIMT), (2) the measurement of the flow-mediated dilation at the brachial artery (FMD), and (3) performing a PCDU. Results Globally, our patients presented with a normal cIMT, FMD and Peak Systolic Value (PSV) in the PCDU. The group with pathological NPTR were significantly older than patients with normal NPTR (55.6 vs 44.1 years, p=0.002), had a higher prevalence of hypertension (50.0 vs 6.8%, p=0.001), longer hypertension duration (8.0 vs 1.3 years, p=0.035), a higher prevalence of diabetes mellitus (42.9 vs 6.8%, p= 0.004), higher blood glucose (111 vs 92 mg/dl, p=0.004) and higher blood triglycerides levels (188 vs 96 mg/dl, p=0.002). Furthermore, they showed lower PSV (46.2 vs 71.0 cm/s, p=0.002) while we did not find significant differences as regards cIMT and FMD between the two groups. Interestingly, the prevalence of patients with pathological PSV (<35 cm/s) was higher among patients with a pathological NPTR but without reaching the statistical significance. However, when adopted an age-dependant cut-off described in the literature (PSV < age x 0.7 + 6.73) the difference between the two groups became significant (50.0 vs 11.4%, p=0.005). Finally, the correlation analysis showed a significant correlation between PSV and the cIMT (-0.352, ρ=0.009), the maximum number of events in the RPTR record (+0.344, ρ=0.012), and the mean tip tumescence (+0.303, ρ=0.036). When corrected for age, these associations lost their statistical significance. Conclusions This study demonstrates the diagnostic value of a pathological NPTR which suggests an organic aetiology of the ED and associates with pathological findings in the PCDU. Age appears to be one of the most important risk factors for ED, even when other risk factors are present. Finally, when using a PCDU to diagnose vasculogenic ED, a cut-off of 35 cm/s might be less sensitive than an age-dependent cut-off to detect early impairments of penile endothelial function. Disclosure No.

Hybrid functional membranes through layer-by-layer assembly of Ti3C2Tx MXene and gelatin-stabilized calcium phosphate nanospheres

MXene-based membranes exhibit promising properties for various applications; however, pure MXene membranes lack sufficient environmental stability and mechanical strength. This work demonstrates that incorporating gelatin-stabilized amorphous calcium phosphate (Gel-ACP) nanospheres into MXene membranes via layer-by-layer assembly significantly enhances flexibility and mechanical strength while retaining electrical conductivity. A pressure-assisted stacking technique was introduced to efficiently fabricate membranes with tunable thickness not achievable through single filtration steps. The Gel-ACP nanospheres, synthesized by co-precipitation of calcium phosphate within gelatin solution, provided a ductile and cytocompatible reinforcement that augmented the properties of the MXene matrix. Direct mixing of the components led to particle aggregation and non-uniform dispersion. In contrast, controlled layer-by-layer nanostructuring maintained membrane conductivity around 102 Scm−1 while dramatically improving mechanical integrity. The optimized hybrid membranes exhibited specific electromagnetic shielding effectiveness of ∼21,000 dBcm2g−1 and withstood over 90 kPa vacuum pressure without rupture, six times higher than pure MXene membranes. Cytocompatibility was confirmed by the proliferation of human mesenchymal stem cells on the membranes. Moreover, the layered membrane exhibited excellent adhesion to bone-mimicking structures, indicating potential utility for bone tissue engineering. Overall, this work provides new design principles for engineering hybrid membranes through controlled multicomponent assembly to overcome intrinsic limitations and impart multifunctionality.

A bi-layer optimization method of the grid-connected microgrid based on the multi-strategy of the beluga whale algorithm

Endeavoring to enhance the penetration rate of renewable energy sources, concurrently ensuring economic and operational stability, this study proposes a novel bi-layer optimization method of the wind–solar-storage AC/DC microgrid (MG). First, by incorporating a superordinate electric and seasonal hydrogen hybrid energy storage system (E&SHESS), the topology structure of the microgrid is established. Subsequently, to rectify the intrinsic limitations of the conventional beluga whale optimization (BWO) algorithm, this paper proposes a multi-strategy hybrid improvement to BWO (MHIBWO). This innovative improvement integrates an MTent strategy, a step size adjustment mechanism, and a crisscross strategy. Then, constructing a bi-layer iterative model based on the topology, annual net income and grid-connected friendliness are introduced as optimization objectives for the outer and inner layers, respectively, utilizing MHIBWO and CPLEX for resolution. Through a nested iteration of the two layers, the model outputs the capacity scheme with the best performance of economy and stability. Finally, the simulation unequivocally demonstrated the superiority of MHIBWO and the model proposed. In addition, based on the real data of the Elia power station, the validity of the method in operation is tested using the fuzzy C-means algorithm (FCMA) to extract and aggregate typical days, thereby presenting a sophisticated solution for the field of microgrids optimization configuration.

A critical appreciation of pathway analysis in atherosclerotic disease. Cellular phenotypic plasticity as an illustrative example

The rapid advancements in genome-scale (omics) techniques has created significant opportunities to investigate complex disease mechanisms in tissues and cells. Nevertheless, interpreting -omics data can be challenging, and pathway enrichment analysis is a frequently used method to identify candidate molecular pathways that drive gene expression changes. With a growing number of -omics studies dedicated to atherosclerosis, there has been a significant increase in studies and hypotheses relying on enrichment analysis.This brief review discusses the benefits and limitations of pathway enrichment analysis within atherosclerosis research. We highlight the challenges of identifying complex biological processes, such as cell phenotypic switching, within -omics data. Additionally, we emphasize the need for more comprehensive and curated gene sets that reflect the biological complexity of atherosclerosis.Pathway enrichment analysis is a valuable tool for gaining insights into the molecular mechanisms of atherosclerosis. Nevertheless, it is crucial to remain aware of the intrinsic limitations of this approach. By addressing these weaknesses, enrichment analysis in atherosclerosis can lead to breakthroughs in identifying the mechanisms of disease progresses, the identification of key driver genes, and consequently, advance personalized patient care.

Tailoring Tamm Plasmon Resonances in Dielectric Nanoporous Photonic Crystals.

The fields of plasmonics and photonic crystals (PCs) have been combined to generate model light-confining Tamm plasmon (TMM) cavities. This approach effectively overcomes the intrinsic limit of diffraction faced by dielectric cavities and mitigates losses associated with the inherent properties of plasmonic materials. In this study, nanoporous anodic alumina PCs, produced by two-step sinusoidal pulse anodization, are used as a model dielectric platform to establish the methodology for tailoring light confinement through TMM resonances. These model dielectric mirrors feature highly organized nanopores and narrow bandwidth photonic stopbands (PSBs) across different positions of the spectrum. Different types of metallic films (gold, silver, and aluminum) were coated on the top of these model dielectric mirrors. By structuring the features of the plasmonic and photonic components of these hybrid structures, the characteristics of TMM resonances were studied to elucidate effective approaches to optimize the light-confining capability of this hybrid TMM model system. Our findings indicate that the coupling of photonic and plasmonic modes is maximized when the PSB of the model dielectric mirror is broad and located within the midvisible region. It was also found that thicker metal films enhance the quality of the confined light. Gas sensing experiments were performed on optimized TMM systems, and their sensitivity was assessed in real time to demonstrate their applicability. Ag films provide superior performance in achieving the highest sensitivity (S = 0.038 ± 0.001 nm ppm-1) based on specific binding interactions between thiol-containing molecules and metal films.

Demystifying simulators for educators in healthcare

When entering the world of simulation development and scenario design for the first time, healthcare educators are often confronted with the perplexing diversity of simulation, which includes a vast array of educational experiences for learners. This essay seeks to demystify the growing number of technologies and simulators (commonly known as simulation modalities). Simulators can be classified as computer-based simulation, simulated participants (SP), simulated clinical immersion or procedural simulation, in addition to ‘mixed’ and ‘hybrid’ simulations. Each modality has intrinsic benefits and limitations, but ultimately their use must be guided by the desired learning outcomes of the learning experience, together with the appropriate realism required for the simulation. No matter the simulator being used, the ultimate experience of the learner is still arguably wholly dependent on good curriculum development, instructional design and scenario delivery.

Experimental investigation of the mode I fracture toughness behaviour of timber adhesive joints: The synergistic effect of the adhesive type and the bondline thickness

Timber is a construction material widely used for structural applications. In the last decades, the improvements of both the bonding techniques and the quality of the adhesives enabled to overcome the intrinsic limitations of sawn timber, resulting in a wide spread of engineered wood products (EWPs). Understanding and assessing the bonding quality of structural EWPs has become nowadays a crucial point to assure their load-bearing capacity and even to increase their performance. One of the most revealing properties to assess the bonding quality and behavior is the fracture energy (or toughness), which could be estimated by performing suitable tests. In the present work, the results of an experimental campaign aimed to define the mode I fracture toughness of timber-to-timber adhesive bonding are presented and discussed. The fracture toughness behavior of three different categories of adhesives for structural applications was investigated, namely melamine-urea-formaldehyde (MUF), polyurethane (PU) and phenol-resorcinol-formaldehyde (PRF). In addition, the effect of the adhesive bondline and precrack thickness (0.15 and 0.30 mm) was also investigated. The results revealed different fracture toughness behaviour between the various adhesives and bond line thickness, in terms of both crack initiation and propagation. In fact, the mode-I fracture toughness appears to be highly dependent on the adhesive type, as also seen by the related analysis of variance. The fractured surfaces are also characterized by means of optical microscopy, giving insights about the local failure modes.

TB-DROP: deep learning-based drug resistance prediction of Mycobacterium tuberculosis utilizing whole genome mutations

The most widely practiced strategy for constructing the deep learning (DL) prediction model for drug resistance of Mycobacterium tuberculosis (MTB) involves the adoption of ready-made and state-of-the-art architectures usually proposed for non-biological problems. However, the ultimate goal is to construct a customized model for predicting the drug resistance of MTB and eventually for the biological phenotypes based on genotypes. Here, we constructed a DL training framework to standardize and modularize each step during the training process using the latest tensorflow 2 API. A systematic and comprehensive evaluation of each module in the three currently representative models, including Convolutional Neural Network, Denoising Autoencoder, and Wide & Deep, which were adopted by CNNGWP, DeepAMR, and WDNN, respectively, was performed in this framework regarding module contributions in order to assemble a novel model with proper dedicated modules. Based on the whole-genome level mutations, a de novo learning method was developed to overcome the intrinsic limitations of previous models that rely on known drug resistance-associated loci. A customized DL model with the multilayer perceptron architecture was constructed and achieved a competitive performance (the mean sensitivity and specificity were 0.90 and 0.87, respectively) compared to previous ones. The new model developed was applied in an end-to-end user-friendly graphical tool named TB-DROP (TuBerculosis Drug Resistance Optimal Prediction: https://github.com/nottwy/TB-DROP), in which users only provide sequencing data and TB-DROP will complete analysis within several minutes for one sample. Our study contributes to both a new strategy of model construction and clinical application of deep learning-based drug-resistance prediction methods.

Challenges and opportunities in cell expansion for cultivated meat.

The cultivation of meat using in vitro grown animal stem cells offers a promising solution to pressing global concerns around climate change, ethical considerations, and public health. However, cultivated meat introduces an unprecedented necessity: the generation of mass scales of cellular biomaterial, achieved by fostering cell proliferation within bioreactors. Existing methods for in vitro cell proliferation encounter substantial challenges in terms of both scalability and economic viability. Within this perspective, we discuss the current landscape of cell proliferation optimization, focusing on approaches pertinent to cellular agriculture. We examine the mechanisms governing proliferation rates, while also addressing intrinsic and conditional rate limitations. Furthermore, we expound upon prospective strategies that could lead to a significant enhancement of the overall scalability and cost-efficiency of the cell proliferation phase within the cultivated meat production process. By exploring knowledge from basic cell cycle studies, pathological contexts and tissue engineering, we may identify innovative solutions toward optimizing cell expansion.

Enhanced sensing resolution with microcavity mode oscillation generated by thermal-optic and photorefractive nonlinearity

Optical microcavity has proven its potential for unlabeled sensing. Here, we propose and demonstrate in a lithium niobate on insulator microcavity an enhanced sensing approach enabled by the nonlinear mode oscillation generated by the competition between thermal-optic and photorefractive effect, which breaks the intrinsic limitation in wavelength resolution set by the cavity's optical quality factor. It allows us to perform precise measurements of the mode shifting introduced by a nanoscale scatterer with a signal to noise ratio of 13.1 dB and paves a distinctive way to improve resonance shift resolution in widely studied microcavity sensors with a platform of great integration capability.

Diastology in the intensive care unit: Challenges for the assessment and future directions.

Myocardial dysfunction is common in patients admitted to the intensive care unit (ICU). Septic disease frequently results in cardiac dysfunction, and sepsis represents the most common cause of admission and death in the ICU. The association between left ventricular (LV) systolic dysfunction and mortality is not clear for critically ill patients. Conversely, LV diastolic dysfunction (DD) seems increasingly recognized as a factor associated with poor outcomes, not only in sepsis but also more generally in critically ill patients. Despite recent attempts to simplify the diagnosis and grading of DD, this remains relatively complex, with the need to use several echocardiographic parameters. Furthermore, the current guidelines have several intrinsic limitations when applied to the ICU setting. In this manuscript, we discuss the challenges in DD classification when applied to critically ill patients, the importance of left atrial pressure estimates for the management of patients in ICU, and whether the study of cardiac dysfunction spectrum during critical illness may benefit from the integration of left ventricular and left atrial strain data to improve diagnostic accuracy and implications for the treatment and prognosis.

Reduced low-prevalence visual search detriment with increasing age: Implications for cognitive theories of aging and real-world search tasks.

When performing multiple successive visual searches, low-prevalence targets are at elevated risk of being missed. This has important implications for real-world visual search tasks, such as diagnostic medical imaging (e.g., searching for a cancer) and airport baggage security screening (e.g., searching for a weapon), which are characterized by low-prevalence targets and potentially dire consequences of target misses. Previous work on low-prevalence visual search indicates that individuals who spontaneously respond more slowly miss fewer targets, which has been attributed to higher quitting thresholds predicting better performance. Previous aging research indicates that older adults typically respond more slowly across multiple task contexts. This has been attributed to both intrinsic limitations in processing speed and astrategic trade-off to prioritize accuracy with increasing age. Synthesizing these two separate lines of research, here we tested whether older adults had a higher quitting threshold and/or slower processing speed in low-prevalence visual search, and the consequences of these for the magnitude of the low-prevalence visual search detriment. We recruited a large sample (N = 380) across a range of ages (20-80 years) and had them search for targets under low- and high-prevalence conditions. Older adults had both slower processing speed and higher quitting thresholds. Older adults were moderately less susceptible to the low-prevalence detriment, and this relationship was mediated by countervailing effects: slower processing speed exacerbated older adults' low-prevalence detriment, whereas elevated quitting threshold mitigated it. Theoretical implications for cognitive aging and practical implications for professional visual search tasks are discussed.

A Wind Field Reconstruction from Numerical Weather Prediction Data Based on a Meteo Particle Model

In the present work, a methodology for wind field reconstruction based on the Meteo Particle model (MPM) from numerical weather prediction (NWP) data is presented. The development of specific wind forecast services is a challenging research topic, in particular for what concerns the availability of accurate local weather forecasts in highly populated areas. Currently, even if NWP limited area models (LAMs) are run at a spatial resolution of about 1 km, this level of information is not sufficient for many applications; for example, to support drone operation in urban contexts. The coupling of the MPM with the NWP limited area model COSMO has been implemented in such a way that the MPM reads the NWP output over a selected area and provides wind values for the generic point considered for the investigation. The numerical results obtained reveal the good behavior of the method in reproducing the general trend of the wind speed, as also confirmed by the power spectra analysis. The MPM is able to step over the intrinsic limitations of the NWP model in terms of the spatial and temporal resolution, even if the MPM inherits the bias that inevitably affects the COSMO output.

P160 Integrative transcriptional analysis to identify predictive biomarkers for IBD severity and subtyping

Abstract Background Conventional diagnostic strategies for Inflammatory Bowel Diseases (IBD) heavily rely on invasive biopsy procedures and subjective questionnaires, thereby introducing intrinsic limitations in the context of precision medicine. By understanding the molecular mechanisms behind IBD disease biology, we aim to identify certain signalling pathways relevant for epithelial tissue regeneration under chronic inflammation conditions. Methods To address these limitations, we compared gene expression in a large cohort of IBD patients, utilizing the IBD Plexus database (Crohn’s & Colitis Foundation, US) as well as other publicly available datasets. We further validated our results derived from the transcriptional programs by using patient derived advanced 3D-organoid models (Hospital of Nottingham, UK). These models, derived from ulcerated and normal tissue regions of IBD patients, serve as a sophisticated platform for robust validation, bridging the gap between molecular insights and clinical applications. Results Our primary objective was to discern putative biomarkers that are capable of effectively stratifying IBD subtypes. We further identified the genes that were upregulated exclusively in each subtypes and the gene signature that is elevated in inflamed samples compared to non-inflamed samples. We found that either the chemokine and interleukin (IL-17) signalling pathways which have known roles in recruitment of immune cells thereby perpetuating chronic inflammation and several key chemokines influence the integrity of the epithelial barrier function and regeneration. We are also analysing non-invasive biomarkers by using samples such as blood, plasma and stool and employ machine learning methods to correlate with established clinical indicators such as sCDAI, PRO2, PRO3, and MAYO categories. Conclusion Hence, in identifying specific targets we can introduce novel therapeutic strategies to mitigate inflammation and restore integrity of the gut epithelial barrier. With this research we aim to improve the current IBD diagnostics landscape as well propels it towards precision medicine by offering precise subtyping and predictive markers for disease severity, ultimately contributing to more personalised and effective therapeutics interventions.

Innovative super-resolution in spatial transcriptomics: a transformer model exploiting histology images and spatial gene expression.

Spatial transcriptomics technologies have shed light on the complexities of tissue structures by accurately mapping spatial microenvironments. Nonetheless, a myriad of methods, especially those utilized in platforms like Visium, often relinquish spatial details owing to intrinsic resolution limitations. In response, we introduce TransformerST, an innovative, unsupervised model anchored in the Transformer architecture, which operates independently of references, thereby ensuring cost-efficiency by circumventing the need for single-cell RNA sequencing. TransformerST not only elevates Visium data from a multicellular level to a single-cell granularity but also showcases adaptability across diverse spatial transcriptomics platforms. By employing a vision transformer-based encoder, it discerns latent image-gene expression co-representations and is further enhanced by spatial correlations, derived from an adaptive graph Transformer module. The sophisticated cross-scale graph network, utilized in super-resolution, significantly boosts the model's accuracy, unveiling complex structure-functional relationships within histology images. Empirical evaluations validate its adeptness in revealing tissue subtleties at the single-cell scale. Crucially, TransformerST adeptly navigates through image-gene co-representation, maximizing the synergistic utility of gene expression and histology images, thereby emerging as a pioneering tool in spatial transcriptomics. It not only enhances resolution to a single-cell level but also introduces a novel approach that optimally utilizes histology images alongside gene expression, providing a refined lens for investigating spatial transcriptomics.