Spatial Localization Research Articles

Development of a GIS-based register of biogas plant sites in Lower Saxony, Germany: a foundation for identifying P2G potential

BackgroundAlthough Germany’s biogas capacity accounts for almost 7% of its installed worldwide capacity, the expansion of biogas plants has stagnated owing to the expiry of Germany’s Renewable Energy Sources Act Erneuerbare–Energien–Gesetz (EEG) subsidies for existing biogas plants. Indeed, without alternative concepts such as power-to-gas (P2G) ensuring their continuing operation, many existing biogas plants must close down to ensure their continuous operation. A detailed spatial register of biogas plant sites must be developed to evaluate the potential for further operation (and thereby promote Germany’s sustainable energy transition). In particular, Lower Saxony, a German federal state, was hit hardest by the expiry of subsidies, as there is a lack of spatially high-resolution information to identify which biogas plants have P2G potential as an end-of-subsidy strategy. This study discusses the development of a geographic information system-based register for these plants.MethodsA register was developed using geographic information system (GIS). Spatial data on existing biogas plants in Lower Saxony were selected from the Digital Landscape Model (DLM) data, with additional information coming inter alia from the Marktstammdatenregister, the Germany-wide core energy market data register. The data were merged into a single register for Lower Saxony, and aerial photographs were used to validate the biogas plant site.ResultsA total of 1704 biogas plant sites were identified throughout Lower Saxony. Spatially resolved plant information on production capacity suggests that three quarters are suitable for inclusion in a methanization concept. Because plants at 85% of the sites will no longer be subsidised by 2035, end-of-subsidy strategies will soon become relevant.ConclusionsThe GIS-based analysis is a reliable and low-error method for identifying biogas plant sites in Lower Saxony. Almost all plants were included in the registry. The greatest advantages over existing registers and at the same time the unique characteristics of our register were the exact spatial localisation of the plants and the highly up-to-date nature of the data. The register enables the initial (spatial) identification, characterisation, and analysis of potential sites for P2G end-of-subsidy strategies. Overall, the register has significant potential as an advisory basis.

Research on Textile Tiny Defective Targets Detection Method Based on YOLO-GCW

In the textile quality control system, textile defect detection occupies a central position. In order to solve the problems of numerous model parameters, time-consuming computation, limited precision, and accuracy of tiny features of textile defects in the defect detection process, this paper proposes a textile defect detection method based on the YOLO-GCW network model. First, in order to solve the problem of detection accuracy of tiny defective targets, the CBAM (Convolutional Block Attention Module) attention mechanism was incorporated to guide the model to focus more on the spatial localization information of the defects. Meanwhile, the WIoU (Weighted Intersection over Union) loss function was adopted to enhance model training as well as to improve the detection accuracy, which can also provide a more accurate measure of match between the model-predicted bounding box and the real target to improve the detection capability of tiny defect targets. Consequently, in view of the need for performance optimization and lightweight deployment, the Ghost convolution structure was adopted to replace the traditional convolution for compressing the model parameter scale and promoting the detection speed of complex texture features in textiles. Finally, numerous experiments proved the positive performance of the presented model and demonstrated its efficiency and effectiveness in various scenes.

Spatial Localization of Collagen Hydroxylated Proline Site Variation as an Ancestral Trait in the Breast Cancer Microenvironment.

Collagen stroma interactions within the extracellular microenvironment of breast tissue play a significant role in breast cancer, including risk, progression, and outcomes. Hydroxylation of proline (HYP) is a common post-translational modification directly linked to breast cancer survival and progression. Changes in HYP status lead to alterations in epithelial cell signaling, extracellular matrix remodeling, and immune cell recruitment. In the present study, we test the hypothesis that the breast cancer microenvironment presents unique PTMs of collagen, which form bioactive domains at these sites that are associated with spatial histopathological characteristics and influence breast epithelial cell signaling. Mass spectrometry imaging proteomics targeting collagens were paired with comprehensive proteomic methods to identify novel breast cancer-related collagen domains based on spatial localization and regulation in 260 breast tissue samples. As ancestry plays a significant role in breast cancer outcomes, these methods were performed on ancestry diverse breast cancer tissues. Lumpectomies from the Cancer Genome Atlas (TCGA; n=10) reported increased levels of prolyl 4-hydroxylase subunit alpha-3 (P4HA3) accompanied by spatial regulation of fibrillar collagen protein sequences. A concise set of triple negative breast cancer lumpectomies (n=10) showed spatial regulation of specific domain sites from collagen alpha-1(I) chain. Tissue microarrays identified proteomic alterations around post-translationally modified collagen sites in healthy breast (n=81) and patient matched normal adjacent (NAT; n=76) and invasive ductal carcinoma (n=83). A collagen alpha-1(I) chain domain encompassing amino acids 506-514 with site-specific proline hydroxylation reported significant alteration between patient matched normal adjacent tissue and invasive breast cancer. Functional testing of domain 506-514 on breast cancer epithelial cells showed proliferation, chemotaxis and cell signaling response dependent on site localization of proline hydroxylation within domain 506-514 variants. These findings support site localized collagen HYP forms novel bioactive domains that are spatially distributed within the breast cancer microenvironment and may play a role in ancestral traits of breast cancer.

Physical origin and control of exciton spatial localization in high-κ MOene monolayers under external perturbations

Two-dimensional (2D) materials hold great promise for the next-generation optoelectronics applications, many of which, including solar cell, rely on the efficient dissociation of exciton into free charge carriers. However, photoexcitation in atomically thin 2D semiconductors typically produces exciton with a binding energy of ∼500 meV, an order of magnitude larger than thermal energy at room temperature. This inefficient exciton dissociation can limit the efficiency of photovoltaics. In this study, employing the first principles approach-DFT, GW + BSE, and analytical model, we demonstrate the role of asymmetric halogenation, dielectric environment, and magnetic field in 2D Ti2O MOene as an efficient strategy for regulating exciton binding energy (EBE) towards spontaneous exciton dissociation. Our study goes beyond the exciton ground state and quantifies the degree of spatial delocalization of exciton in excited states as well. We determine the quantitative impact of varying dielectric screening and magnetic field strength on EBE for different excited states (1 s, 2 s, 3 s, 4 s, and so on). Importantly, we reveal the significant role of orbital orientation (whether in-plane or out-of-plane) and symmetry (related to the angular momentum quantum number) in understanding the spatial localization of excitons and their binding energy. Additionally, a high dielectric constant in 2D MOene enables easier exciton dissociation, similar to that observed in 3D bulk semiconductors, while also harnessing the advantages of 2D materials. This makes it an effective material that combines the best of both 3D bulk and 2D structures. The study offers a promising strategy for designing next-generation optoelectronic devices.

Reconstruction of local three-dimensional temperature field of tumor cells with low-toxic nanoscale quantum-dot thermometer and cepstrum spatial localization algorithm

The optimal method for three-dimensional thermal imaging within cells involves collecting intracellular temperature responses while simultaneously obtaining corresponding 3D positional information. Current temperature measurement techniques based on the photothermal properties of quantum dots face several limitations, including high cytotoxicity and low fluorescence quantum yields. These issues affect the normal metabolic processes of tumor cells. This study synthesizes a low-toxicity cell membrane-targeted quantum dot temperature sensor by optimizing the synthesis method of CdTe/CdS/ZnS core-shell structured quantum dots. Compared to CdTe-targeted quantum dot temperature sensors, the cytotoxicity of CdTe/CdS/ZnS-targeted quantum dot temperature sensors is reduced by 40.79%. Additionally, a novel cepstrum-based spatial localization algorithm is proposed to achieve rapidly compute the three-dimensional positions of densely distributed quantum dot temperature sensors. Ultimately, both targeted and non-targeted CdTe/CdS/ZnS quantum dot temperature sensors were used simultaneously to label the internal and external regions of human osteosarcoma cells to obtain temperature data at these labeling positions. By combining this with the cepstrum-based spatial localization algorithm, the spatial coordinates of the quantum dot temperature sensors were obtained. Three-dimensional temperature field reconstruction of three local regions was achieved within a 12 μm axial range in living cells. The method described in this paper can be widely applied to the quantitative study of intracellular thermal responses.

Camellia saponin modulates oleic acid/linoleic acid-induced lipogenesis in human sebocytes through lipophagy activation.

Oily skin not only threatens people with aesthetic and hygienic discomfort but also confronts them with annoying skin problems. To explore new skin care ingredients from herbal or plant extracts and understand their underlying mechanism for sebum control would assist in the discovery of desirable sebosuppressive agents, though it is still a deserving and challenging task. To explore the effect of Camellia saponin (CS) on modulating the lipogenesis of human sebocytes. Moreover, to explore the underlying mechanism of CS on oleic acid/linoleic acid (OL) mixture stimulated lipid accumulation. The lipid accumulation model of cells was constructed by OL-induction invitro. The lipid synthesis in SZ95 sebocytes was detected by Oil Red O, Nile Red and BODIPY staining and the distribution of lipid droplets and autophagosomes were evaluated by transmission electron microscopy (TEM). Fluorescence staining, immunofluorescence and western blot (WB) were used to characterize the spatial localization of lipid droplets (LDs)/autophagosome/lysosome, the levels of LC3 and P62 proteins related to intracellular autophagy, as well as the pH of lysosome. CS treatment significantly relieved OL-induced lipid accumulation in SZ95 sebocytes. Furthermore, CS maintained lysosomal acid environment to promote the fusion of autophagosome and lysosome, thus recovering the OL-induced blockage of autophagy flow. We also found that CS activated AMPK, and down-regulated mTOR in SZ95 sebocytes. CS was able to relieve OL-stimulated sebum accumulation in cultured human SZ95 sebocytes through lipophagy, in which process CS maintained lysosomal acid environment and activated the AMPK/mTOR pathway.

Identification of ARHGEF11 (PDZ-RhoGEF) as an in vivo regulator of synapses and cognition

Given the influence of cognitive abilities on life outcomes, there is inherent value in identifying genes involved in controlling learning and memory. Further, cognitive dysfunction is a core feature of many neuropsychiatric disorders. Here, we use a combinatory in silico approach to identify human gene targets that will have an especially high likelihood of individually and directly impacting cognition. This broad and unbiased screen led to the specific identification of ARHGEF11, which encodes PDZ-RhoGEF. PDZ-RhoGEF is a largely RhoA-specific activator that is highly enriched in dendritic spines, and recent work identified hyperexpression of PDZ-RhoGEF in the prefrontal cortex of bipolar disorder subjects, a disease characterized by an early emergence and persistence of broad scope cognitive dysfunction. Here, we characterize the effects of PDZ-RhoGEF on synaptic and behavioral phenotypes, and we identify molecular and biochemical mechanisms that control PDZ-RhoGEF's expression, synaptic spatial localization, and enzymatic activity. Importantly, our identified direct regulators of PDZ-RhoGEF (miR-132 and DISC1) have themselves been repeatedly implicated in controlling cognitive phenotypes in humans, including those caused by several neuropsychiatric disorders. Taken together, our findings indicate that PDZ-RhoGEF is a key convergence point among multiple synaptic and cognition-relevant signaling cascades with potential translational significance.

Deep-learning based electromagnetic navigation system for transthoracic percutaneous puncture of small pulmonary nodules

Percutaneous transthoracic puncture of small pulmonary nodules is technically challenging. We developed a novel electromagnetic navigation puncture system for the puncture of sub-centimeter lung nodules by combining multiple deep learning models with electromagnetic and spatial localization technologies. We compared the performance of DL-EMNS and conventional CT-guided methods in percutaneous lung punctures using phantom and animal models. In the phantom study, the DL-EMNS group showed a higher technical success rate (95.6% vs. 77.8%, p = 0.027), smaller error (1.47 ± 1.62 mm vs. 3.98 ± 2.58 mm, p < 0.001), shorter procedure duration (291.56 ± 150.30 vs. 676.44 ± 246.12 s, p < 0.001), and fewer number of CT acquisitions (1.2 ± 0.66 vs. 2.93 ± 0.98, p < 0.001) compared to the traditional CT-guided group. In the animal study, DL-EMNS significantly improved technical success rate (100% vs. 84.0%, p = 0.015), reduced operation time (121.36 ± 38.87 s vs. 321.60 ± 129.12 s, p < 0.001), number of CT acquisitions (1.09 ± 0.29 vs. 2.96 ± 0.73, p < 0.001) and complication rate (0% vs. 20%, p = 0.002). In conclusion, with the assistance of DL-EMNS, the operators got better performance in the percutaneous puncture of small pulmonary nodules.

Two‐Level Spatially Localized Proximity Labeling for Cross‐Biological‐Hierarchy Measurement and Manipulation

Proximity labeling (PL) has emerged as a powerful technique for the in situ elucidation of biomolecular interaction networks. However, PL methods generally rely on single‐biological‐hierarchy control of spatial localization at the labeling site, which limits their application in multi‐tiered biological systems. Here, we introduced another enzymatic reaction upstream of an enzyme‐based PL reaction and targeted the two enzymes to markers indicating different biological hierarchies, establishing a two‐level spatially localized proximity labeling (P2L) platform for in situ molecular measurement and manipulation. Using the cellular‐ and glycan‐level as the hierarchical models, we demonstrated the ability of P2L to efficiently execute a two‐step logic operation and to discriminate target cells with different levels of glycosylation within mixed cell populations. By mounting clickable handles via P2L, we reprogrammed the robust covalent assembly of cells at designated sites. The combination of P2L with proteomics led to the profiling of the protein microenvironment of specific glycans on target cells, revealing changes in tumor‐cell‐surface interactions under immune pressure from a glycan perspective. P2L provides not only a solution for revealing the heterogeneity of biological systems, but also new insights in the fields of intelligent logic computation, enzyme engineering, tissue engineering, etc.

Two-Level Spatially Localized Proximity Labeling for Cross-Biological-Hierarchy Measurement and Manipulation.

Proximity labeling (PL) has emerged as a powerful technique for the in situ elucidation of biomolecular interaction networks. However, PL methods generally rely on single-biological-hierarchy control of spatial localization at the labeling site, which limits their application in multi-tiered biological systems. Here, we introduced another enzymatic reaction upstream of an enzyme-based PL reaction and targeted the two enzymes to markers indicating different biological hierarchies, establishing a two-level spatially localized proximity labeling (P2L) platform for in situ molecular measurement and manipulation. Using the cellular- and glycan-level as the hierarchical models, we demonstrated the ability of P2L to efficiently execute a two-step logic operation and to discriminate target cells with different levels of glycosylation within mixed cell populations. By mounting clickable handles via P2L, we reprogrammed the robust covalent assembly of cells at designated sites. The combination of P2L with proteomics led to the profiling of the protein microenvironment of specific glycans on target cells, revealing changes in tumor-cell-surface interactions under immune pressure from a glycan perspective. P2L provides not only a solution for revealing the heterogeneity of biological systems, but also new insights in the fields of intelligent logic computation, enzyme engineering, tissue engineering, etc.

Cyclic Peak Extraction from a Spatial Likelihood Map for Multi-Array Multi-Target Bearing-Only Localization

In the context of multi-array multi-target bearing-only localization, due to the existence of direction-finding errors, the crossing results of bearing lines cannot accurately determine correspondence with targets. Under conditions that clutter interference and missing of detection in direction-finding, the traditional method will produce false alarm targets and miss some targets. To address this issue, this paper draws on the idea of a spatial likelihood map which calculates the likelihood of target presence at each grid point within the observation area by partitioning the observation area into grids and utilizing bearing data from each array, yielding the distribution of targets in the observation area. Then, a multi-target cyclic peak extraction algorithm based on a statistical dual-threshold is proposed, which eliminates false peaks by cyclic extraction of target positions, so as to reduce false targets. Simulation results demonstrate that the spatial likelihood mapping-based localization exhibits good performance. Furthermore, when the multi-target cyclic peak extraction algorithm based on statistical dual-thresholds is applied, it outperforms direct target extraction from the spatial likelihood map, showcasing enhanced multi-target localization capabilities. Moreover, compared to the position non-linear least squares multi-target localization method, the proposed method has lower optimal sub-pattern assignment distance and lower localization error under the condition of interference and missing detection.

Quantifying spectral information about source separation in multisource odour plumes.

Odours released by objects in natural environments can contain information about their spatial locations. In particular, the correlation of odour concentration timeseries produced by two spatially separated sources contains information about the distance between the sources. For example, mice are able to distinguish correlated and anti-correlated odour fluctuations at frequencies up to 40 Hz, while insect olfactory receptor neurons can resolve fluctuations exceeding 100 Hz. Can this high-frequency acuity support odour source localization? Here we answer this question by quantifying the spatial information about source separation contained in the spectral constituents of correlations. We used computational fluid dynamics simulations of multisource plumes in two-dimensional chaotic flow environments to generate temporally complex, covarying odour concentration fields. By relating the correlation of these fields to the spectral decompositions of the associated odour concentration timeseries, and making simplifying assumptions about the statistics of these decompositions, we derived analytic expressions for the Fisher information contained in the spectral components of the correlations about source separation. We computed the Fisher information for a broad range of frequencies and source separations for three different source arrangements and found that high frequencies were more informative than low frequencies when sources were close relative to the sizes of the large eddies in the flow. We observed a qualitatively similar effect in an independent set of simulations with different geometry, but not for surrogate data with a similar power spectrum to our simulations but in which all frequencies were a priori equally informative. Our work suggests that the high-frequency acuity of olfactory systems may support high-resolution spatial localization of odour sources. We also provide a model of the distribution of the spectral components of correlations that is accurate over a broad range of frequencies and source separations. More broadly, our work establishes an approach for the quantification of the spatial information in odour concentration timeseries.

Transcranial brain atlas based on photon measurement density function in a triple-parameter standard channel space.

Functional near-infrared spectroscopy (fNIRS) is a widely-used transcranial brain imaging technique in neuroscience research. Nevertheless, the lack of anatomical information from recordings poses challenges for designing appropriate optode montages and for localizing fNIRS signals to underlying anatomical regions. The photon measurement density function (PMDF) is often employed to address these issues, as it accurately measures the sensitivity of an fNIRS channel to perturbations of absorption coefficients at any brain location. However, existing PMDF-based localization methods have two limitations: (1) limited channel space, and (2) estimation based on a single standard head model, which usually differ anatomically from individuals. To overcome these limitations, this study proposes a continuous standard channel space for fNIRS and constructs a PMDF-based transcranial brain atlas (PMDF-TBA) by calculating PMDFs using MRI data from 48 adults. The PMDF-TBA contains group-averaged sensitivities of channels to gray matter and brain regions as defined in 3 atlases: Brodmann, AAL2, and LPBA40. We evaluated the prediction ability of PMDF-TBA for sensitivity of unseen individuals. The results show that it outperformed PMDFs based on single standard head models, making PMDF-TBA a more generalizable fNIRS spatial localization tool. Therefore, in the absence of individual sMRI data, PMDF-TBA can optimize optode montage design, enhance channel sensitivity in target brain regions, and assist in source localization for fNIRS data, thereby facilitating the application of fNIRS in neuroscience research.

A Novel Map Matching Method Based on Improved Hidden Markov and Conditional Random Fields Model

ABSTRACT Aiming at the inherent “labeling bias” problem of Hidden Markov Models (HMMs) in the process of low-frequency sampling rate GPS trajectory map matching, this paper proposes a map matching method that combines HMMs and Conditional Random Field Models (CRFs). Various features including vehicle driving direction, direction change between candidate roads, shortest path, and spatial localization accuracy of trajectory points are integrated to optimize the state transfer process in the HMM model. The Viterbi algorithm was then employed to calculate the joint probability values of all candidate paths and the path with maximum probability value was selected as the matching path. The proposed map matching method was validated using publicly available Global Positioning System (GPS) vehicle trajectories from Guangzhou, Dongguan, and field-collected vehicle trajectory data from Ganzhou, China. The results demonstrated that the proposed method achieves high matching accuracy while maintaining efficient matching efficiency. The percentage of correct matching of GPS sampling points remains above 95%, and the average calculation time for each candidate road segment is kept within 50 ms. These advancements can greatly benefit location-based services that rely on map matching technology.

Three-dimensional spatial localization and volume estimation of prostate tumors using 18F-PSMA-1007 PET/CT versus multiparametric MRI.

Fluorine-18 prostate-specific membraneantigen-1007 positron emission tomography/computed tomography (18F-PSMA-1007 PET/CT) has been shown to be superior to multiparametric magnetic resonance imaging (MRI) for the locoregional staging of intermediate-risk and high-risk prostate tumors. This study aims to evaluate whether it is also superior in estimating tumor parameters, such as three-dimensional spatial localization and volume. 134 participants underwent 18F-PSMA-1007 PET/CT and MRI prior to radical prostatectomy as part of the validating paired-cohort Next Generation Trial (NCT05141760). MRI, 18F-PSMA-1007 PET/CT, and final pathology were independently assessed by blinded radiologists, nuclear medicine physicians, and pathologists, respectively. Individual tumor nodules were measured in three dimensions and cognitively registered to 38 segment prostate diagrams as per PI-RADSv2.1. Correct spatial localization was compared using McNemar test and estimation of tumor volumes were compared using linear regression and partial F-test. 286 tumor nodules were identified by final histopathology. 18F-PSMA-1007 PET/CT was superior to MRI for correct localization (186 [65.0%] vs 134 [46.9%], p < 0.001) and tumor volume estimation (R2 = 0.545 vs 0.431, p < 0.001). Larger tumors and higher Gleason Grade Group (GGG) were associated with correct localization by 18F-PSMA-1007 PET/CT (OR = 2.05, p < 0.001 for tumor volume and OR = 4.92, p < 0.01 for ≥ GGG3) and MRI (OR = 1.81, p < 0.001 for tumor volume and OR = 11.67, p < 0.001 for ≥ GGG3). 18F-PSMA-1007 PET/CT outperforms MRI for determination of three-dimensional spatial localization and volume of prostate tumors. These findings support the use of 18F-PSMA-1007 PET/CT prior to definitive treatment of localized prostate cancers.

Three-Dimensional Prospective Modeling and Deep Metallogenic Prediction of the Lintan Gold Deposit in Guizhou Province, China

The Lintan gold deposit, located in the “gold triangle” of Qianxinan, Guizhou Province, has become a focal point for implementing the “exploring near existing deposits” strategy, aiming to identify another large-scale gold deposit within the region. This study addresses the challenges of deep-edge mineral exploration in the Lintan gold deposit by adopting a metallogenic system perspective. Using a multidisciplinary approach, it integrates geological, geophysical, and geochemical datasets to construct various three-dimensional (3D) visualization and prospectivity models. The research leverages geostatistical theories and methods, multisource digital information analysis, and advanced 3D modeling and visualization techniques to verify mineralization anomalies. These efforts expand the scope of prospectivity evaluation for the deep-edge regions of the Lintan gold deposit into 3D space. In the 3D spatial framework, this study elucidates the metallogenic geological characteristics, geophysical anomalies, and geochemical signatures within the study area. Building upon this foundation, it conducts a comprehensive analysis and evaluation of geological, geochemical, and geophysical prospecting indicators under multisource geoscience datasets. This approach transitions from known to unknown domains, effectively reducing the ambiguities and uncertainties associated with single-source data interpretations. The findings demonstrate that, under the guidance of geological prospectivity models, the effective integration and synthesis of geological, geophysical, and geochemical data can reveal the interrelationships between metallogenic geological bodies and the contributing factors of the metallogenic system. This enables the identification of anomalous information associated with metallogenic geological bodies and facilitates the spatial localization and prediction of target areas for deep-edge mineral resources. The proposed methodology provides novel insights and techniques for deep-edge mineral exploration. Comprehensive analysis indicates significant prospectivity for mineral resource exploration in the deep-edge regions of the Lintan gold deposit.

Numerical Modelling of Myocardial Infarction in Multivessel Coronary Lesion. II. Patterns of Formation of Large-Scale Damages and Structures

In this work, the basic mechanisms of myocardial infarction development during its inflammatory phase have been studied using mathematical modelling methods. Complex scenarios associated with multivessel lesions of the coronary bed and variability in baseline indicators of the innate immune system state have been considered. Special attention is paid to methodological issues related to the analysis of the effectiveness of the algorithm for approximate solutions of nonlinear initial-boundary value problems and setting up computational experiments under conditions close to the conditions of laboratory experiments in the field of interest. A numerical analysis was performed of several of the most common variants in laboratory practice of the formation of a large lesion in the left ventricle of the mouse heart, caused by the spatiotemporal heterogeneity of the properties of the environment, the immune reaction and ischemic myocardial damage, including recurrent infarction. The main attention is on the following aspects: – analysis of the mechanism of formation of large-scale infarct damages with an extensive core covering almost the entire infarct focus, or with a relatively small core, and assessment of the role of inflammation in these processes; – analysis of current scenarios of structure formation and the role of nonlinear dynamic structures of demarcation inflammation in the formation of a large but highly structured focus. The obtained data allow us to conclude that there is sufficient conservatism during infarction, evidenced by the basic patterns of the inflammatory phase revealed during modeling. The variability of heart attack scenarios manifests itself as a «memory»-effect about the initial data. We observe the «memory»-effect only at a biologically significant time interval, but we also note a general tendency to switch to the usual scenario of inflammation in large-focal infarction, which eliminates the peculiarities of the initial and individual conditions. The role of inflammation has been investigated in the context of a wave process in which spatial localization and the interaction of density and concentration waves can determine the main features of myocardial damage. In particular, within the framework of the adopted mathematical model, the conditions under which the formation of local zones with a relatively low or, conversely, high level of damage is possible have been described. In our work, we have established a significant interdependence between the formation of quasi-stationary structures and the intensity of the immune response. The high probability of developing severe or even terminal myocardial infarction may be due to the high level of immune system factors, in particular, monocytes-macrophages or cytokines in the reinfarction heart.

Effects of attentional focus on spatial localization of distal body parts and touch in two-arm position matching.

This study investigated how the judgment of proximal joint position can be affected by touch alone, focused attention on the distal body part, or touch spatial localization. Participants completed a two-arm elbow joint position-matching task, in which they indicated the location of one forearm by the placement of the other. In four test conditions, matching was performed during (1) detection of touch (tactile stimulation of index finger pads), (2) spatial localization of fingers (attention focused on the position of index finger pads), (3) spatial localization of touch on fingers (attention focused on tactile stimulation of index finger pads), and (4) detection of touch but localization of fingers (tactile stimulation of index finger pads, but attention focusing on the spatial position of the pads). In the first experiment (n = 23), the sensitivity of muscle spindle receptors in both reference and indicator arms was reduced and equalized by both-slack conditioning. In the second experiment (n = 20), the illusion of excessive elbow flexion in the reference arm and excessive extension in the indicator arm was generated through extension-flexion conditioning. In the first experiment, the accuracy and precision of matching were unaffected in any test condition. In the second experiment, participants made amplified undershooting errors under attention-focused conditions. In conclusion, focused attention on the location of a distal body part and touch affects both the spatial localization of the limb and tactile remapping only when the perceived forearm position is misinterpreted due to imbalanced proprioceptive input from antagonistic arm muscles.

New insights into the role of mitophagy related gene affecting the metastasis of osteosarcoma through scRNA-seq and CRISPR-Cas9 genome editing

BackgroundOsteosarcoma (OSA), the most common primary bone malignancy, poses significant challenges due to its aggressive nature and propensity for metastasis, especially in adolescents. Mitophagy analysis can help identify new therapeutic targets and combined treatment strategies.MethodsThis study integrates single-cell sequencing (scRNA-seq) data and bulk-seq to identify mitophagy-related genes (MRGs) associated with the progression of OSA metastasis and analyze their clinical significance. scRNA-seq data elucidates the relationship between mitophagy and OSA metastasis, employing “CellChat” R package to explore intercellular communications and report on hundreds of ligand-receptor interactions. Subsequently, the combination of bulk-seq and CRISPR-Cas9 gene editing identifies mitophagy-related biomarker associated with metastatic prognosis. Finally, validation of the relationship between mitophagy and OSA metastasis is achieved through cellular biology experiments and animal studies.ResultsThe distinct mitophagy activity of various mitochondria manifests in diverse spatial localization, cellular developmental trajectories, and intercellular interactions. OSA tissue exhibits notable heterogeneity in mitophagy within osteoblastic OSA cells. However, high mitophagy activity correlates consistently with high metastatic potential. Subsequently, we identified three critical genes associated with mitophagy in OSA, namely RPS27A, TOMM20 and UBB. According to the aforementioned queue of genes, we have constructed a mitophagy_score (MIP_score). We observed that it consistently predicts patient prognosis in both internal and external datasets, demonstrating strong robustness and stability. Furthermore, we have found that MIP_score can also guide chemotherapy, with varying sensitivities to chemotherapeutic agents based on different MIP_score. It is noteworthy that, through the integration of CRISPR-Cas9 genome-wide screening and validation via cellular and animal experiments, we have identified RPS27A as a potential novel biomarker for OSA.ConclusionsOur comprehensive analysis elucidated the profile of mitophagy throughout the OSA metastasis process, forming the basis for a mitophagy-related prognostic model that addresses clinical outcomes and drug sensitivity following OSA metastasis. Additionally, an online interactive platform was established to assist clinicians in decision-making (https://mip-score.shinyapps.io/labtan/). These findings lay the groundwork for developing targeted therapies aimed at improving the prognosis of OSA patients.

Deep Learning-Enabled Integration of Histology and Transcriptomics for Tissue Spatial Profile Analysis.

Spatially resolved transcriptomics enable comprehensive measurement of gene expression at subcellular resolution while preserving the spatial context of the tissue microenvironment. While deep learning has shown promise in analyzing SCST datasets, most efforts have focused on sequence data and spatial localization, with limited emphasis on leveraging rich histopathological insights from staining images. We introduce GIST, a deep learning-enabled gene expression and histology integration for spatial cellular profiling. GIST employs histopathology foundation models pretrained on millions of histology images to enhance feature extraction and a hybrid graph transformer model to integrate them with transcriptome features. Validated with datasets from human lung, breast, and colorectal cancers, GIST effectively reveals spatial domains and substantially improves the accuracy of segmenting the microenvironment after denoising transcriptomics data. This enhancement enables more accurate gene expression analysis and aids in identifying prognostic marker genes, outperforming state-of-the-art deep learning methods with a total improvement of up to 49.72%. GIST provides a generalizable framework for integrating histology with spatial transcriptome analysis, revealing novel insights into spatial organization and functional dynamics.