Spatial Localization Research Articles

Localized stem structures in quasi-resonant two-soliton solutions for the asymmetric Nizhnik–Novikov–Veselov system

Elastic collisions of solitons generally have a finite phase shift. When the phase shift has a finitely large value, the two vertices of the (2 + 1)-dimensional two-soliton are significantly separated due to the phase shift, accompanied by the formation of a local structure connecting the two V-shaped solitons. We define this local structure as the stem structure. This study systematically investigates the localized stem structures between two solitons in the (2 + 1)-dimensional asymmetric Nizhnik–Novikov–Veselov system. These stem structures, arising from quasi-resonant collisions between the solitons, exhibit distinct features of spatial locality and temporal invariance. We explore two scenarios: one characterized by weakly quasi-resonant collisions (i.e. a12 ≈ 0), and the other by strongly quasi-resonant collisions (i.e. a12 ≈ +∞). Through mathematical analysis, we extract comprehensive insights into the trajectories, amplitudes, and velocities of the soliton arms. Furthermore, we discuss the characteristics of the stem structures, including their length and extreme points. Our findings shed new light on the interaction between solitons in the (2 + 1)-dimensional asymmetric Nizhnik–Novikov–Veselov system.

HOUSING PRICES AS PAYMENT FOR ECOSYSTEM SERVICES OF URBAN GREEN SPACES: CASE STUDY IN KYIV

The purpose of the study is to analyze the impact of the availability of urban green spaces (UGS) and air quality on the cost of residential real estate in Kyiv. Additionally, it seeks to identify the level of awareness among city residents regarding the advantages of living close to UGSs in areas with relatively clean air and their willingness to pay a higher price for such housing. The QGIS software was employed to assess the pedestrian and time accessibility levels of recreational green spaces, and the results are depicted on the corresponding map. The least accessible for everyday recreation is the UGS in the central districts of the city, which traditionally have the highest population density and the most expensive residential real estate. The article analyzes the priority criteria for determining the housing cost, depending on the spatial localization relative to the city center and the comfort class of real estate. The distribution of housing across different tiers and the price range for the apartments in new constructions in Kyiv during 2015–2021 were also examined. To identify the correlation between the cost of residential real estate in the capital’s new buildings and the availability of green spaces nearby, we investigated the spatial distribution of prices in the largest residential complexes in the city. The results of spatial visualization obtained through Voronoi diagrams or Delaunay triangulation indicate significant disparities in the cost of housing located in the center of Kyiv or on the periphery. Logically, residential complexes with the highest apartment prices are located in the central districts of Kyiv and near the metro stations. However, most of these locations lack accessible green areas suitable for everyday recreation. Therefore, there is no positive correlation between housing prices and the presence of UGSs nearby. To achieve the research goal, we also conducted a sociological survey among Kyiv residents in various districts of the city. The survey results indicate that most Kyiv residents do not have the desire or are not currently willing to pay a higher price for the opportunity to live near a park or forest and breathe clean air, i.e., to enjoy the ecosystem services provided by UGSs fully. The scientific novelty of the research lies in determining the pedestrian and time accessibility of recreational green spaces in Kyiv, identifying trends in spatial clustering of the most expensive residential real estate in central city areas, and demonstrating the lack of a positive impact of the availability of green areas on the per square meter cost of housing in new developments. Additionally, the study reveals a remarkably low willingness of the city residents to pay a higher price for the opportunity to receive ecosystem services from available UGSs.

Spatial Localization of Transformer Inspection Robot Based on Adaptive Denoising and SCOT-β Generalized Cross-Correlation.

In the detection process of the internal defects of large oil-immersed transformers, due to the huge size of large transformers and metal-enclosed structures, the positional localization of miniature inspection robots inside the transformer faces great difficulties. To address this problem, this paper proposes a three-dimensional positional localization method based on adaptive denoising and the SCOT weighting function with the addition of the exponent β (SCOT-β) generalized cross-correlation for L-type ultrasonic arrays of transformer internal inspection robots. Aiming at the strong noise interference in the field, the original signal is decomposed by an improved Empirical Mode Decomposition (EMD) method, and the optimal center frequency and bandwidth of each mode are adaptively searched. By extracting the modes in the frequency band of the positional localization signal, suppressing the modes in the noise frequency band, and reconstructing the Intrinsic Mode Function (IMF) of the independently selected superior modal components, a signal with a high signal-to-noise ratio is obtained. In addition, for the traditional mutual correlation algorithm with a large delay estimation error at a low signal-to-noise ratio, this paper adopts an improved generalized joint weighting function, SCOT-β, which improves the anti-jamming ability of the generalized mutual correlation method at a low signal-to-noise ratio by adding an exponential function to the denominator term of the SCOT weighting function's generalized cross-correlation. Finally, the accurate positional localization of the transformer internal inspection robot is realized based on the quadratic L-array and search-based maximum likelihood estimation method. Simulation and experimental results show the following: the improved EMD denoising method better improves the signal-to-noise ratio of the positional localization signal with a lower distortion rate; in the transformer test tank, which is 120 cm in length, 100 cm in width, and 100 cm in height, based on the positional localization method in this paper, the average relative positional localization error of the transformer internal inspection robot in three-dimensional space is 2.27%, and the maximum positional localization error is less than 2 cm, which meets the requirements of engineering positional localization.

Cryo-Fluorescence Tomography as a Tool for Visualizing Whole-Body Inflammation Using Perfluorocarbon Nanoemulsion Tracers.

We explore the use of intravenously delivered fluorescent perfluorocarbon (PFC) nanoemulsion tracers and multi-spectral cryo-fluorescence tomography (CFT) for whole-body tracer imaging in murine inflammation models. CFT is an emerging technique that provides high-resolution, three-dimensional mapping of probe localization in intact animals and tissue samples, enabling unbiased validation of probe biodistribution and minimizes reliance on laborious histological methods employing discrete tissue panels, where disseminated populations of PFC-labeled cells may be overlooked. This methodology can be used to streamline the development of new generations of non-invasive, cellular-molecular imaging probes for in vivo imaging. Mixtures of nanoemulsions with different fluorescent emission wavelengths were administered intravenously to naïve mice and models of acute inflammation, colitis, and solid tumor. Mice were euthanized 24 h post-injection, frozen en bloc, and imaged at high resolution (~ 50 µm voxels) using CFT at multiple wavelengths. PFC nanoemulsions were visualized using CFT within tissues of the reticuloendothelial system and inflammatory lesions, consistent with immune cell (macrophage) labeling, as previously reported in in vivo magnetic resonance and nuclear imaging studies. The CFT signals show pronounced differences among fluorescence wavelengths and tissues, presumably due to autofluorescence, differential fluorescence quenching, and scattering of incident and emitted light. CFT is an effective and complementary methodology to in vivo imaging for validating PFC nanoemulsion biodistribution at high spatial localization, bridging the resolution gap between in vivo imaging and histology.

EEG emotion recognition using EEG-SWTNS neural network through EEG spectral image

The rapid development in deep learning models provide considerable advancements in emotion recognition by electroencephalogram (EEG). However, existing approaches primarily focus on temporal and frequency domain features of EEG signals, neglecting spatial domain features in model information integration. Therefore, this study proposed a novel EEG input format, called EEG spectral image (ESI), which integrates spatial domain features using Azimuthal Equidistant Projection (AEP) and frequency domain features through differential entropy (DE). To better utilize this fine-grained data format, we propose the EEG Swin Transformer (EEG-SWTNS), which combines the window attention mechanism with shifted window partitioning. Window attention mechanism can concentrate on affective information within various regions and use shifted window partitioning to disrupt aggregated emotional representations for more granular features. Different from traditional graph neural networks, this method leverages the spatial locality of region information, leading to enhanced performance in EEG-based emotion recognition. Experiments conducted on SEED and SEED IV datasets demonstrate superior performance compared to baseline methods and the state-of-the-art models. Relative improvements of 0.6% and 0.08% are observed in subject-dependent experiments, while accuracies of 80.07% and 66.72% are achieved in subject-independent experiments without utilizing transfer learning techniques.

Factors in the Effective Use of Hearing Aids among Subjects with Age-Related Hearing Loss: A Systematic Review.

Objectives: Investigate factors contributing to the effective management of age-related hearing loss (ARHL) rehabilitation. Methods: A systematic review was conducted following PRISMA guidelines. The protocol was registered in PROSPERO (CRD42022374811). Articles were identified through systematic searches in the Scopus, PubMed, Web of Science, and Cochrane databases in May 2024. Only articles published between January 2005 and May 2024 were included. Studies were assessed for eligibility by two independent researchers and evaluated using the Crowe Critical Appraisal Tool v1.4 (CCAT). Results: Of the 278 articles identified, 54 were included. Three factors explain effective HA use. First, hearing aid signal processing, with directional microphones and noise reduction, improves user comfort and understanding regarding noise. Second, there is hearing aid fitting, with the NAL prescription rules as the gold standard, and bilateral, high-level HA performance for spatial localization and noise comprehension. Third, there is a patient-centered approach, using patient-related outcome measures (PROMs), questionnaires, counseling, and regular follow-up to involve patients in their therapeutic rehabilitation. Conclusions: Reaching a consensus on acoustic parameters is challenging due to variability in audiological results. Involving patients in their rehabilitation, addressing their needs and expectations, and offering individualized care are crucial.

Mass Spectrometry Imaging Reveals Region-Specific Lipid Alterations in the Mouse Brain in Response to Efavirenz Treatment.

Efavirenz (EFV) is a commonly used drug to treat human immunodeficiency virus infection and is known to exert adverse effects on the brain. Although it is known that EFV is associated with abnormal plasma lipid levels, the changes in the spatial localization of individual lipid molecules in brain tissue following EFV treatment are yet to be explored. In this study, we employed a matrix-assisted laser desorption/ionization mass spectrometry imaging approach to determine region-specific lipid alterations in mouse brains following EFV treatment. We detected unique spatial localization patterns of phosphatidylcholine (PC), sphingomyelin (SM), ceramide phosphoinositol (PI-Cer), and hexosylceramide (HexCer) molecules in the mouse brain. Interestingly, PC(32:0), PC(38:5), and SM(36:1;O2) showed high abundance in the hippocampus region, whereas PI-Cer(38:8) exhibited low abundance in the hippocampus region of the EFV-treated mouse brains. Additionally, we observed low abundance of PC(38:6), PC(40:6), and PI-Cer(40:3) in the thalamus region of the EFV-treated mouse brains. Furthermore, SM(40:1;O2), SM(42:2;O2), SM(42:1;O2), SM(43:2;O2), and SM(43:1;O2) exhibited their accumulation in the corpus callosum region of the EFV-treated mouse brains as compared to controls. However, HexCer(42:1;O3) exhibited depletion in the corpus callosum region in response to EFV treatment. To characterize the expression patterns of proteins, including lipid metabolizing enzymes, in response to EFV treatment, mass spectrometry-based proteomics was utilized. From these, the expression levels of 12 brain proteins were found to be significantly decreased following EFV treatment. Taken together, these multiomics data provide important insights into the effects of EFV on brain lipid metabolism.

Multiplexed imaging to reveal tissue dendritic cell spatial localisation and function.

Dendritic cells (DCs) play a pivotal role in immune surveillance, acting as sentinels that coordinate immune responses within tissues. Although differences in the identity and functional states of DC subpopulations have been identified through multiparametric flow cytometry and single-cell RNA sequencing, these methods do not provide information about the spatial context in which the cells are located. This knowledge is crucial for understanding tissue organisation and cellular cross-talk. Recent developments in multiplex imaging techniques can now offer insights into this complex spatial and functional landscape. This review provides a concise overview of these imaging methodologies, emphasising their application in identifying DCs to delineate their tissue-specific functions and aiding newcomers in navigating this field.

CD163+ macrophages in mantle cell lymphoma induce activation of prosurvival pathways and immune suppression

AbstractMantle cell lymphoma (MCL) is dependent on a supportive tumor immune microenvironment (TIME) in which infiltration of CD163+ macrophages has a negative prognostic impact. This study explores how abundance and spatial localization of CD163+ cells are associated with the biology of MCL, using spatial multiomic investigations of tumor and infiltrating CD163+ and CD3+ cells. A total of 63 proteins were measured using GeoMx digital spatial profiling in tissue microarrays from 100 diagnostic MCL tissues. Regions of interest were selected in tumor-rich and tumor-sparse tissue regions. Molecular profiling of CD163+ macrophages, CD20+ MCL cells, and CD3+ T-cells was performed. To validate protein profiles, 1811 messenger RNAs were measured in CD20+ cells and 2 subsets of T cells. Image analysis was used to extract the phenotype and position of each targeted cell, thereby allowing the exploration of cell frequencies and cellular neighborhoods. Proteomic investigations revealed that CD163+ cells modulate their immune profile depending on their localization and that the immune inhibitory molecules, V-domain immunoglobulin suppressor of T-cell activation and B7 homolog 3, have higher expression in tumor-sparse than in tumor-rich tissue regions and that targeting should be explored. We showed that MCL tissues with more abundant infiltration of CD163+ cells have a higher proteomic and transcriptional expression of key components of the MAPK pathway. Thus, the MAPK pathway may be a feasible therapeutic target in patients with MCL with CD163+ cell infiltration. We further showed the independent and combined prognostic values of CD11c and CD163 beyond established risk factors.

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.