Spatial Localization Research Articles

Optimization and validation of multi-echo, multi-contrast SAGE acquisition in fMRI.

The purpose of this study was to optimize and validate a multi-contrast, multi-echo fMRI method using a combined spin- and gradient-echo (SAGE) acquisition. It was hypothesized that SAGE-based blood oxygen level-dependent (BOLD) functional MRI (fMRI) will improve sensitivity and spatial specificity while reducing signal dropout. SAGE-fMRI data were acquired with five echoes (2 gradient-echoes, 2 asymmetric spin-echoes, and 1 spin-echo) across 12 protocols with varying acceleration factors, and temporal SNR (tSNR) was assessed. The optimized protocol was then implemented in working memory and vision tasks in 15 healthy subjects. Task-based analysis was performed using individual echoes, quantitative dynamic relaxation times T2 * and T2, and echo time-dependent weighted combinations of dynamic signals. These methods were compared to determine the optimal analysis method for SAGE-fMRI. Implementation of a multiband factor of 2 and sensitivity encoding (SENSE) factor of 2.5 yielded adequate spatiotemporal resolution while minimizing artifacts and loss in tSNR. Higher BOLD contrast-to-noise ratio (CNR) and tSNR were observed for SAGE-fMRI relative to single-echo fMRI, especially in regions with large susceptibility effects and for T2-dominant analyses. Using a working memory task, the extent of activation was highest with T2 *-weighting, while smaller clusters were observed with quantitative T2 * and T2. SAGE-fMRI couples the high BOLD sensitivity from multi-gradient-echo acquisitions with improved spatial localization from spin-echo acquisitions, providing two contrasts for analysis. SAGE-fMRI provides substantial advantages, including improving CNR and tSNR for more accurate analysis.

PD-1/PD-L1 interaction score and NKT-like cell infiltration predict immunotherapy efficacy in non-small cell lung cancer patients

ObjectiveThe currently available biomarkers are insufficient to accurately predict the immunotherapy response in patients. This work attempted to investigate effects of PD-1/PD-L1 interaction score combined with NKT-like cell infiltration level in tumor microenvironment on predicting immunotherapy efficacy. Methods24 non-small cell lung cancer (NSCLC) patients who underwent immunotherapy were analyzed using multiplex immunofluorescence to quantitatively assess positive cells of target biomarkers and their spatial localization. Correlation between PD-1/PD-L1 interaction score in combination with NKT-like cell infiltration level and immunotherapy response was analyzed. The predictive performance of two individual biomarkers and combined novel biomarkers in immunotherapy efficacy was assessed through receiver operating characteristic curve analysis. Relationships between these factors and patient survival prognosis were analyzed using Kaplan–Meier curves. ResultsAmong responders, PD-1/PD-L1 interaction score and NKT-like cell infiltration level were significantly higher than nonresponders (P < 0.05), and PD-1/PD-L1 interaction score and NKT-like cell infiltration level could effectively identify the population with immunotherapy response, with area under the curves (AUCs) of 0.7571 and 0.8643, respectively. Combination of the two had the best performance in predicting the efficacy of immunotherapy (AUC = 0.9070). High PD-1/PD-L1 interaction scores and high levels of NKT-like cell infiltration significantly improved progression-free survival (HR = 0.2544, P = 0.0053) and overall survival (HR = 0.2820, P = 0.0053) in patients. ConclusionsCombination of PD-1/PD-L1 interaction score and NKT-like cell infiltration level had favorable performance in predicting immunotherapy response in NSCLC patients, contributing to accurately identify patients who may benefit from immunotherapy.

Machine learning methods in automated detection of CT enterography findings in Crohn's disease: A feasibility study

PurposeQualitative findings in Crohn's disease (CD) can be challenging to reliably report and quantify. We evaluated machine learning methodologies to both standardize the detection of common qualitative findings of ileal CD and determine finding spatial localization on CT enterography (CTE). Materials and methodsSubjects with ileal CD and a CTE from a single center retrospective study between 2016 and 2021 were included. 165 CTEs were reviewed by two fellowship-trained abdominal radiologists for the presence and spatial distribution of five qualitative CD findings: mural enhancement, mural stratification, stenosis, wall thickening, and mesenteric fat stranding. A Random Forest (RF) ensemble model using automatically extracted specialist-directed bowel features and an unbiased convolutional neural network (CNN) were developed to predict the presence of qualitative findings. Model performance was assessed using area under the curve (AUC), sensitivity, specificity, accuracy, and kappa agreement statistics. ResultsIn 165 subjects with 29,895 individual qualitative finding assessments, agreement between radiologists for localization was good to very good (κ = 0.66 to 0.73), except for mesenteric fat stranding (κ = 0.47). RF prediction models had excellent performance, with an overall AUC, sensitivity, specificity of 0.91, 0.81 and 0.85, respectively. RF model and radiologist agreement for localization of CD findings approximated agreement between radiologists (κ = 0.67 to 0.76). Unbiased CNN models without benefit of disease knowledge had very similar performance to RF models which used specialist-defined imaging features. ConclusionMachine learning techniques for CTE image analysis can identify the presence, location, and distribution of qualitative CD findings with similar performance to experienced radiologists.

Spatial distribution pattern of immune cells is associated with patient prognosis in colorectal cancer

BackgroundThe spatial context of tumor-infiltrating immune cells (TIICs) is important in predicting colorectal cancer (CRC) patients’ clinical outcomes. However, the prognostic value of the TIIC spatial distribution is unknown. Thus, we aimed to investigate the association between TIICs in situ and patient prognosis in a large CRC sample.MethodsWe implemented multiplex immunohistochemistry staining technology in 190 CRC samples to quantify 14 TIIC subgroups in situ. To delineate the spatial relationship of TIICs to tumor cells, tissue slides were segmented into tumor cell and microenvironment compartments based on image recognition technology, and the distance between immune and tumor cells was calculated by implementing the computational pipeline phenoptr.ResultsMPO+ neutrophils and CD68+IDO1+ tumor-associated macrophages (TAMs) were enriched in the epithelial compartment, and myeloid lineage cells were located nearest to tumor cells. Except for CD68+CD163+ TAMs, other cells were all positively associated with favorable prognosis. The prognostic predictive power of TIICs was highly related to their distance to tumor cells. Unsupervised clustering analysis divided colorectal cancer into three subtypes with distinct prognostic outcomes, and correlation analysis revealed the synergy among B cells, CD68+IDO1+TAMs, and T lineage cells in producing an effective immune response.ConclusionsOur study suggests that the integration of spatial localization with TIIC abundance is important for comprehensive prognostic assessment.

Design of photonic topological corner states for electrically pumped terahertz quantum cascade laser

Photonic topological corner states (PTCS) represent discrete zero modes whose frequencies stay in the topological bandgap and wavefunctions exhibit strong spatial localization, which is highly desirable for efficient surface-emitting lasers. While being extensively investigated in optically pumped lasers, the electrically pumped PTCS lasers are merely reported due to the lack of suitable configurations. In this paper, we propose a photonic cavity design based on the PTCSs that can be applied for electrically pumped terahertz quantum cascade lasers (QCLs). The cavity is constructed by following the canonical trivial-nontrivial domain, where the nontrivial domain with expanded and merged four QCL rods is surrounded by the trivial domain with shrunk and merged four QCL rods. Both domains follow the 2D Su–Schrieffer–Heeger (SSH) model. To further construct a fabrication-favorable 3D device, the designed QCL rod array is connected by 2D veins, which functionalize simultaneously as the intermedium for enhanced inter-/intra-cell coupling and the conductive connection to the device. The 3D numerical calculation is finally investigated.

Spectroscopic and Interferometric Sum-Frequency Imaging of Strongly Coupled Phonon Polaritons in SiC Metasurfaces.

Phonon polaritons enable waveguiding and localization of infrared light with extreme confinement and low losses. The spatial propagation and spectral resonances of such polaritons are usually probed with complementary techniques such as near-field optical microscopy and far-field reflection spectroscopy. Here, infrared-visible sum-frequency spectro-microscopy is introduced as a tool for spectroscopic imaging of phonon polaritons. The technique simultaneously provides sub-wavelength spatial resolution and highly-resolved spectral resonance information. This is implemented by resonantly exciting polaritons using a tunable infrared laser and wide-field microscopic detection of the upconverted light. The technique is employed to image hybridization and strong coupling of localized and propagating surface phonon polaritons in a metasurface of SiC micropillars. Spectro-microscopy allows to measure the polariton dispersion simultaneously in momentum space by angle-dependent resonance imaging, and in real space by polariton interferometry. Notably, it is possible to directly image how strong coupling affects the spatial localization of polaritons, inaccessible with conventional spectroscopic techniques. The formation of edge states is observed at excitation frequencies where strong coupling prevents polariton propagation into the metasurface. The technique is applicable to the wide range of polaritonic materials with broken inversion symmetry and can be used as a fast and non-perturbative tool to image polariton hybridization andpropagation.

Integration of transcriptomics and spatial biology analyses reveals Galactomyces ferment filtrate promotes epidermal interconnectivity via induction of keratinocyte differentiation, proliferation and cellular bioenergetics.

Human skin is the first line of defence from environmental factors such as solar radiation and is susceptible to premature ageing, including a disruption in epidermal differentiation and homeostasis. We evaluated the impact of a Galactomyces Ferment Filtrate (GFF) on epidermal differentiation and response to oxidative stress. We used transcriptomics, both spatial and traditional, to assess the impact of GFF on epidermal biology and homeostasis in keratinocytes (primary or immortalized) and in exvivo skin explant tissue. The effect of GFF on cell adhesion rates, cellular ATP levels and proliferation rates were quantitated. Oxidative phosphorylation and glycolytic rates were measured under normal and stress-induced conditions. Transcriptomics from keratinocytes and exvivo skin explants from multiple donors show GFF induces keratinocyte differentiation, skin barrier development and cell adhesion while simultaneously repressing cellular stress and inflammatory related processes. Spatial transcriptomics profiling of exvivo skin indicated basal keratinocytes at the epidermal-dermal junction and cornifying keratinocytes in the top layer of the epidermis as the primary cell types influenced by GFF treatment. Additionally, GFF significantly increases crosstalk between suprabasal and basal keratinocytes. To support these findings, we show that GFF can significantly increase cell adhesion and proliferation in keratinocytes. GFF also protected overall cellular bioenergetics under metabolic or oxidative stress conditions. Our findings provide novel insights into cellular differences and epidermal spatial localization in response to GFF, supporting previous findings that this filtrate has a significant impact on epidermal biology and homeostasis, particularly on spatially defined crosstalk. We propose that GFF can help maintain epidermal health by enhancing keratinocyte crosstalk and differentiation/proliferation balance as well as promoting an enhanced response to stress.

IMMU-06. CAR DESIGN SHAPES THE BRAIN TUMOR IMMUNE MICROENVIRONMENT: IMPACT ON ACTIVITY AND PERSISTENCE OF ANTI-GLIOMA ADOPTIVE T CELL IMMUNOTHERAPY

Abstract BACKGROUND B7-H3 CAR T-cells effectively eradicate brain tumors in xenograft models and have proven safe in clinical trials, yet no objective responses were observed in patients. The limited efficacy is partly attributed to the immunosuppressive tumor microenvironment (TME), a characteristic not fully recapitulated in investigations using xenograft models. In this study, we set to optimize B7-H3 CAR structure and examine the effects of CAR design on the brain TME using an immunocompetent glioma model. METHODS We generated a panel of fully murine B7-H3 CARs with variations in transmembrane, co-stimulatory, and activation domains. This enabled us to comprehensively investigate their effector functions within the intact immune system. High-dimensional flow cytometry and single-cell RNA sequencing were then used to investigate changes in the brain TME following CAR T-cell therapy. RESULTS While five out of six B7-H3 CARs with single co-stimulatory domains exhibited potent functionality in vitro, optimizing co-stimulation and signaling failed to enhance their anti-glioma efficacy in vivo. To further enhance therapeutic effectiveness and persistence, we incorporated 4-1BB and CD28 co-stimulation through transgenic expression of 4-1BBL on CD28-based CAR T-cells. Although this design significantly improved anti-glioma efficacy in vitro, it conferred no additional benefits in vivo. Analysis of the TME revealed that CAR T-cell therapy influenced the immune composition of the brain TME. Successful anti-tumor responses were dictated by the recruitment, activation, and spatial localization of unique inflammatory ‘immune hubs’ containing specific subsets of macrophages and endogenous T-cells. Interestingly, depletion of brain macrophages using CSF1R inhibitor markedly inhibited CAR T-cell anti-tumor activity. CONCLUSION Our study highlights the critical role of CAR structural design and its modulation of the TME in mediating the efficacy of CAR T-cell therapy in brain tumors. Yet, more specific targeting and/or reprogramming of the TME is warranted for the successful design of effective adoptive immunotherapies for high-grade glioma.

Functional dissociation of the language network and other cognition in early childhood.

Is language distinct from other cognition during development? Does neural machinery for language emerge from general-purpose neural mechanisms, becoming tuned for language after years of experience and maturation? Answering these questions will shed light on the origins of domain-specificity in the brain. We address these questions using precision fMRI, scanning young children (35 months to 9 years of age) on an auditory language localizer, spatial working memory localizer (engaging the domain-general multiple demand [MD] network), and a resting-state scan. We create subject-specific functional regions of interest for each network and examine their selectivity, specificity, and functional connectivity. We find young children show domain-specific, left-lateralized language activation, and that the language network is not responsive to domain-general cognitive load. Additionally, the cortically adjacent MD network is selective to cognitive load, but not to language. These networks show higher within versus between-network functional connectivity. This connectivity is stable across ages (examined cross-sectionally and longitudinally), whereas language responses increase with age and across time within subject, reflecting a domain-specific developmental change. Overall, we provide evidence for a double dissociation of the language and MD network throughout development, in both their function and connectivity. These findings suggest that domain-specificity, even for uniquely human cognition like language, develops early and distinctly from mechanisms that presumably support other human cognition.

Enhanced Photobiocatalytic Cascades at Pickering Droplet Interfaces.

Developing new methods to engineer photobiocatalytic reactions is of utmost significance for artificial photosynthesis, but it remains a grand challenge due to the intrinsic incompatibility of biocatalysts with photocatalysts. In this work, photocatalysts and enzymes were spatially colocalized at Pickering droplet interfaces, where the reaction microenvironment and the spatial distance between two distinct catalysts were exquisitely regulated to achieve unprecedented photobiocatalytic cascade reactions. As proof of the concept, ultrathin graphitic carbon nitride nanosheets loaded with Au nanoparticles were precisely positioned in the outer interfacial layer of Pickering oil droplets to produce H2O2 under light irradiation, while enzymes were exactly placed in the inner interfacial layer to catalyze the subsequent biocatalytic oxidation reactions using in situ formed H2O2 as an oxidant. In the alkene epoxidation and thioether oxidation, our interfacial photobiocatalytic cascades showed a 2.0-5.8-fold higher overall reaction efficiency than the photobiocatalytic cascades in the bulk water phase. It was demonstrated that spatial localization of the photocatalyst and the enzyme at Pickering oil droplet interfaces not only provided their respective preferable reaction environments and intimate proximity for rapid H2O2 transport but also protected the enzyme from oxidative inactivation caused by the photogenerated species. These remarkable interfacial effects contributed to the significantly enhanced photobiocatalytic cascading efficiency. Our work presents an innovative photobiocatalytic reaction system with manifold benefits, providing a cutting-edge platform for solar-driven chemical transformations via photobiocatalysis.

Innovative methodology for noninvasive spatial mapping of gold nanoparticle distribution in tissues: potential applications in biomedical imaging and therapy

Gold nanoparticles (AuNPs) have emerged as versatile agents in biomedical applications, particularly for enhancing contrast in tagged biological tissues for tumor imaging and diagnostics due to their strong absorption cross-section. In this study, we present a methodology for quantifying the spatial distribution of AuNPs within superficial tissue volumes. Utilizing silicone tissue phantoms as a background medium and spatial frequency domain imaging (SFDI) to measure the tissues’ optical properties, we constructed a lookup table (LUT) to infer the optical properties of embedded AuNPs with varying spatial concentrations and depths across multiple spatial frequencies. An analytical solution derived from the LUT facilitated the determination of embedded NP concentration in-depth as a function of measured spatial frequency-dependent optical absorption. Notably, SFDI enabled the spatial localization of NPs in three dimensions. These findings lay the foundation for future in vivo studies on mapping NPs and hold significant promise for advancing biomedical imaging techniques.

Spatial localization of hippocampal replay requires dopamine signaling.

Sequenced reactivations of hippocampal neurons called replays, concomitant with sharp-wave ripples in the local field potential, are critical for the consolidation of episodic memory, but whether replays depend on the brain's reward or novelty signals is unknown. Here we combined chemogenetic silencing of dopamine neurons in ventral tegmental area (VTA) and simultaneous electrophysiological recordings in dorsal hippocampal CA1, in freely behaving rats experiencing changes to reward magnitude and environmental novelty. Surprisingly, VTA silencing did not prevent ripple increases where reward was increased, but caused dramatic, aberrant ripple increases where reward was unchanged. These increases were associated with increased reverse-ordered replays. On familiar tracks this effect disappeared, and ripples tracked reward prediction error, indicating that non-VTA reward signals were sufficient to direct replay. Our results reveal a novel dependence of hippocampal replay on dopamine, and a role for a VTA-independent reward prediction error signal that is reliable only in familiar environments.

Dual analysis of IL-8 in non-small cell lung cancer: A clinical perspective on protein and RNA evaluation via imaging mass cytometry.

e15068 Background: The cytokine Interleukin-8 (IL-8) plays a pivotal role in the tumorigenesis and progression of non-small cell lung cancer (NSCLC), influencing both the tumor microenvironment and immune escape mechanisms. Given its significance, accurately assessing IL-8 levels at the protein and RNA levels could provide valuable insights into its prognostic value and inform therapeutic targeting strategies. This study utilizes imaging mass cytometry (IMC) to conduct a comprehensive evaluation of IL-8 expression in NSCLC, integrating both protein presence and gene expression analysis within the spatial context of the tumor microenvironment. Methods: Our approach leverages the capabilities of IMC to simultaneously detect protein and RNA levels of IL-8 in NSCLC tissue samples. For protein analysis, antibodies tagged with heavy metal isotopes specifically target IL-8, allowing for precise quantification and spatial localization within the tumor. Concurrently, RNA detection is achieved through hybridization probes designed for IL-8 mRNA, enabling the direct observation of gene expression patterns. This method provides a unique opportunity to correlate IL-8's protein abundance with its mRNA expression, offering insights into the regulatory mechanisms at play and their impact on the tumor's behavior and patient outcomes. Results: Preliminary data demonstrate a marked heterogeneity in IL-8 distribution at both the protein and RNA levels, with distinct patterns emerging that relate to tumor aggressiveness and immune infiltration. High IL-8 expression, identified through our integrated IMC approach, correlates with poor prognosis and reduced response to standard therapies, underscoring its potential as a biomarker for NSCLC management. Furthermore, the spatial analysis afforded by IMC highlights the microenvironmental niches where IL-8-mediated interactions could drive tumor progression and immune evasion. Conclusions: By employing IMC for the dual analysis of IL-8 protein and RNA in NSCLC, our study provides a comprehensive view of this cytokine's role in lung cancer pathology. The ability to visualize and quantify IL-8 expression within the spatial context of the tumor microenvironment opens new avenues for understanding its biological impact and for developing targeted therapies. Our findings advocate for the inclusion of IL-8 in the biomarker panel for NSCLC, with potential implications for patient stratification and personalized treatment planning. Future research will focus on expanding these observations to a larger cohort and exploring the therapeutic modulation of IL-8 as a strategy for improving NSCLC outcomes.

Enrichment of glioblastoma tumor microenvironment in a GZMK+ T cell population.

2065 Background: Glioblastoma (GBM) bears a survival estimate below 10% at 5 years, despite standard chemoradiation treatment. Trials evaluating immune checkpoint blockade (ICB) in gliomas have been disappointing so far, dealing with the unique immunosuppressive tumor microenvironment (TME) of the brain. Nevertheless, we have clinical evidence of ICB efficacy for brain metastases (BrM) of immunosensitive tumors. Even if both GBMs and BrM share the same organ-specific microenvironment, it is clear that disease-specific immune characteristics instruct differential response to ICB. Methods: By using advanced deep learning algorithms to combine public and in-house-generated single-cell RNA sequencing profiles, we analyzed immune infiltrate characteristics of 602949 CD45+ cells from 76 GBM, 30 BrM from different types of extracranial tumors (13 NSCLC, 12 melanoma, 2 ovarian, 1 breast, 1 colorectal and 1 renal carcinoma) and 3 normal brain samples. The analysis was restricted to newly diagnosed GBM, excluding recurrent and pre-treated cases. CD8+ cells underwent segregation into clusters based on log2 normalized expression levels of CD3E and CD8A (with expression values greater than 0). The data integration utilized DESC, a deep learning model founded on autoencoders, and subsequent examination was conducted within the Scanpy framework. Cluster analysis was executed employing the Leiden algorithm. Differentially expressed genes (DEGs) were pinpointed across clusters using the Wilcoxon test, with False Discovery Rate (FDR) correction carried out through the Benjamini-Hochberg procedure. Results: From CD45+CD3+ cells, we isolated CD8+ T cells and identified 7 informative CD8+ T cells clusters. Based on DEGs, we could appreciate T cells clusters that were also recapitulated in other extracranial malignancies. The most abundant subset was composed of GZMK+ effector T cells (Cluster 0: GZMK, GZMM, CD44, KLRG1, IL7R, GZMA, CXCR3), followed by putative tumor-specific tissue-resident memory cells (Cluster 1: GZMB, LAG3, CTLA4, TIGIT, CXCL13, ITGAE, ENTPD1, PDCD1, CD27, HLA-DRB1), stem-cell memory-like T cells (Cluster 3: IL7R, CCR7, JUNB, KLF2, TCF7) and a less represented cluster characterized by high expression of genes belonging to the HSP family (Cluster 4: HSPA1, HSPH1, HSPE1, HSPD1, IFN). Interestingly, while the majority of the identified clusters was equally represented in the 3 different tissues, the GZMK+ effector T cells cluster (Cluster 0) was found to be more represented in GBM compared to BrM and normal brain tissue. Conclusions: A GZMK+ effector T cells subpopulation, specifically enriched in GBM, has been poorly characterized in its function and spatial localization. Our data highlight the importance of studying the composition of the immune infiltrate in GBM with multi-omics approaches in order to uncover deep mechanisms of resistance to ICB and eventually provide hints on potential therapeutic targets.

MISP-Fuse: A progressive fusion network guided by Multi-Information supervision

The fusion of infrared and visible images aims to synthesize an image that combines the advantageous characteristics of both image sources. Nevertheless, existing image fusion algorithms often struggle to extract relevant feature information from the source images and find it challenging to balance the significance of different modality information within the source images. This leads to suboptimal fusion results and inadequately serve advanced downstream visual tasks. In order to confront this challenge, a novel image fusion algorithm, denoted as Multiple Information Supervised Progressive Fusion Network (MISP-Fuse), has been proposed. Specifically, MISP-Fuse employs an innovative and robust multi-stage encoder-decoder network called Full Scale Feature Residual network (FSFR) to extract spatial context and detailed feature information corresponding to infrared and visible images. Within this encoder-decoder network, source image feature information is progressively extracted at different stages, which are recombined at multiple scales and distilled for crucial information. Finally, the fusion image is generated through a spatial localization module named as Spatial Localization Network (SLNet). MISP-Fuse incorporates a multi-information supervision mechanism to establish a linkage between different modality information in the infrared and visible source images. It ensures that the resulting fused image not only aligns with human visual perception but also effectively serves advanced downstream visual tasks. The comparative experiments utilizing diverse image fusion benchmark datasets has been conducted. In comparison to other algorithms, MISP-Fuse demonstrated significant enhancements in comprehensive image fusion metrics, including Average Gradient (AG), Sum of Correlated Differences (SCD), and Correlation Coefficient (CC).

Where Are Czech Adolescents Active? The Patterns of Movement and Transport Behavior in Different Active Living Domains.

To understand the environmental determinants of physical activity (PA), precise spatial localization is crucial. This cross-sectional study focuses on the spatiotemporal distribution of PA among Czech adolescents (n = 171) using Global Positioning System loggers and accelerometers. The results showed that adolescents spent most of their time in sedentary behavior, with 57.2% and 58.5% of monitored time at home and school, respectively. The park and playground had the lowest proportion of sedentary behavior but also the lowest amount of moderate to vigorous PA (MVPA). However, when considering the time spent in each domain, the highest proportion of MVPA was seen in publicly accessible playgrounds (13.3% of the time). Chi-square analysis showed that the relative distribution of different PA intensities did not differ across spatial domains. Based on these results, the authors propose 2 key strategies for increasing MVPA in adolescents: Increase the time spent in activity-supportive environments, such as parks and playgrounds, and design techniques to increase MVPA at home and school settings.

MAD-Former: A Traceable Interpretability Model for Alzheimer's Disease Recognition Based on Multi-Patch Attention.

The integration of structural magnetic resonance imaging (sMRI) and deep learning techniques is one of the important research directions for the automatic diagnosis of Alzheimer's disease (AD). Despite the satisfactory performance achieved by existing voxel-based models based on convolutional neural networks (CNNs), such models only handle AD-related brain atrophy at a single spatial scale and lack spatial localization of abnormal brain regions based on model interpretability. To address the above limitations, we propose a traceable interpretability model for AD recognition based on multi-patch attention (MAD-Former). MAD-Former consists of two parts: recognition and interpretability. In the recognition part, we design a 3D brain feature extraction network to extract local features, followed by constructing a dual-branch attention structure with different patch sizes to achieve global feature extraction, forming a multi-scale spatial feature extraction framework. Meanwhile, we propose an important attention similarity position loss function to assist in model decision-making. The interpretability part proposes a traceable method that can obtain a 3D ROI space through attention-based selection and receptive field tracing. This space encompasses key brain tissues that influence model decisions. Experimental results reveal the significant role of brain tissues such as the Fusiform Gyrus (FuG) in AD recognition. MAD-Former achieves outstanding performance in different tasks on ADNI and OASIS datasets, demonstrating reliable model interpretability.

Enzymes encapsulated in organic–inorganic hybrid nanoflower with spatial localization for sensitive and colorimetric detection of formate

Formate is an important environmental pollutant, and meanwhile its concentration change is associated with a variety of diseases. Thus, rapid and sensitive detection of formate is critical for the biochemical analysis of complex samples and clinical diagnosis of multiple diseases. Herein, a colorimetric biosensor was constructed based on the cascade catalysis of formate oxidase (FOx) and horseradish peroxidase (HRP). These two enzymes were co-immobilized in Cu3(PO4)2-based hybrid nanoflower with spatial localization, in which FOx and HRP were located in the shell and core of nanoflower, respectively (FOx@HRP). In this system, FOx could catalyze the oxidation of formate to generate H2O2, which was then utilized by HRP to oxidize 2,2′-azino-bis-3-ethylbenzothiazoline-6-sulphonic acid to yield blue product. Ideal linear correlation could be obtained between the absorbance at 420 nm and formate concentration. Meanwhile, FOx@HRP exhibited excellent detection performance with low limit of detection (6 μM), wide linear detection range (10–900 μM), and favorable specificity, stability and reusability. Moreover, it could be applied in the detection of formate in environmental, food and biological samples with high accuracy. Collectively, FOx@HRP provides a useful strategy for the simple and sensitive detection of formate and is potentially to be used in biochemical analysis and clinical diagnosis.

ScAnalyzer: an image processing tool to monitor plant disease symptoms and pathogen spread in Arabidopsis thaliana leaves

BackgroundPlants are known to be infected by a wide range of pathogenic microbes. To study plant diseases caused by microbes, it is imperative to be able to monitor disease symptoms and microbial colonization in a quantitative and objective manner. In contrast to more traditional measures that use manual assignments of disease categories, image processing provides a more accurate and objective quantification of plant disease symptoms. Besides monitoring disease symptoms, computational image processing provides additional information on the spatial localization of pathogenic microbes in different plant tissues.ResultsHere we report on an image analysis tool called ScAnalyzer to monitor disease symptoms and bacterial spread in Arabidopsis thaliana leaves. Thereto, detached leaves are assembled in a grid and scanned, which enables automated separation of individual samples. A pixel color threshold is used to segment healthy (green) from chlorotic (yellow) leaf areas. The spread of luminescence-tagged bacteria is monitored via light-sensitive films, which are processed in a similar manner as the leaf scans. We show that this tool is able to capture previously identified differences in susceptibility of the model plant A. thaliana to the bacterial pathogen Xanthomonas campestris pv. campestris. Moreover, we show that the ScAnalyzer pipeline provides a more detailed assessment of bacterial spread within plant leaves than previously used methods. Finally, by combining the disease symptom values with bacterial spread values from the same leaves, we show that bacterial spread precedes visual disease symptoms.ConclusionTaken together, we present an automated script to monitor plant disease symptoms and microbial spread in A. thaliana leaves. The freely available software (https://github.com/MolPlantPathology/ScAnalyzer) has the potential to standardize the analysis of disease assays between different groups.

Spatial acoustic emission localization method for damage to prestressed steel strands of girders using special-shaped waveguide rods

As an essential load-bearing member of post-tensioned prestressed girders, prestressed steel strands will inevitably reduce the safety and durability of the bridge if damaged. However, traditional damage localization methods for the steel strand require numerous acoustic emission (AE) sensors with a complex arrangement in the prestressed girder. This study proposes a spatial AE localization method using a dual-sensor array with the assistance of special-shaped waveguide rods. The spatial damage localization is achieved by combining the time difference of arrival (TDOA) of the AE wave with the propagation attenuation law. Transverse and longitudinal damage localization for prestressed steel strands are verified on a simplified platform containing different special-shaped waveguide rods. For transverse damage localization, the TDOA of the maximum AE amplitudes is superior to that of the first waves. For longitudinal damage localization, the relative localization accuracy reaches 94%. With this method, only two AE sensors are required to localize the three-dimensional damage to the prestressed steel strands in a girder.