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PubMed2026

Spatial Proteomics of the Normal Breast Collagen Stroma: Links to Density and Body Mass Index.

Collagen breast stroma can become a breast cancer risk factor, yet proteomic regulation of normal breast stroma remains poorly defined. This study evaluates the spatial regulation of the collagen proteome from normal breast tissue. Normal breast tissue sections from the Susan G. Komen tissue bank were used (n = 40), with data including genetic ancestry (n = 20 African ancestry; n = 20 European ancestry), body-mass-index (BMI), age, and mammogram density by the Breast Imaging Reporting and Data System (BI-RADS). 10-plex cell marker staining showed CD44 and COL1A1 markers modulated with BMI. Collagen fiber widths by second harmonic generation microscopy contrasted in BMI categories by genetic ancestry. Targeted extracellular matrix proteomics mass spectrometry imaging showed the collagen alpha-1(I) chain proteome was spatially heterogeneous across the normal breast microenvironment with site-specific post-translational modification of proline hydroxylation. Signatures computationally extracted from stroma-rich regions reported that 47 collagen peptides distinguished BI-RADS categories (area under the receiver operating curve >0.7; p-value >0.05). Multivariate modeling of collagen peptides, fiber metrics, and clinical features supported a strong positive association with BMI as a determinant of collagen alterations in the normal breast. This study provides a foundation for larger studies investigating the clinical value of spatial collagen proteome alterations in human breast.

Humans↗
PubMed2026

Light-activated CRISPR/dCas9 nanomedicine for programmable control of renal fibrosis.

Renal fibrosis is the final common pathway of progressive chronic kidney disease and is maintained by spatially heterogeneous interactions among injured epithelial cells, activated fibroblasts, immune cells, extracellular matrix remodeling, metabolic stress, and persistent profibrotic transcriptional programs. Current therapies slow renal functional decline but do not directly control the regulatory circuits that stabilize maladaptive repair. Photoresponsive renal nanomedicine offers a potential strategy to add external control to anti-fibrotic intervention by combining kidney-directed delivery with light-gated release or activation of molecular payloads. This review examines the emerging interface between photoresponsive nanomaterials and CRISPR/dCas9-based gene regulation for renal fibrosis, with emphasis on upconversion nanoparticles, photoresponsive polymers, ROS- and pH-responsive matrices, optogenetic switches, and renal-compartment-directed carrier design. We argue that the most defensible therapeutic objective is not permanent genome editing or autonomous organ regeneration, but spatially confined, temporally limited, and reversible regulation of validated fibrotic or protective gene programs using CRISPRa, CRISPRi, or dCas9-based epigenome editors. The review therefore evaluates material requirements, optical-dosimetry constraints, payload architecture, renal biodistribution, target-selection logic, safety risks, and preclinical validation criteria. By defining the engineering and biological conditions required for controlled anti-fibrotic regulation, this framework positions photoresponsive renal nanomedicine as a translationally testable route toward localized modulation of fibrotic cell states rather than an overextended claim of kidney regeneration.

Anti-fibrotic gene regulation↗
PubMed2025

Advances and challenges in human 3D solid tumor models.

The field of cancer biology and therapeutics has soared in the past several decades with new therapeutic modalities and options for patients, such as chemoradiotherapy, immunotherapy, and combination therapy. This dramatic success in expanding patient options is primarily attributed to the development of various model systems to elucidate drivers of oncogenesis, tumor maturation and evolution, and response to therapeutics. While mouse models have been a workhorse of cancer research, technological progress in ex vivo patient-derived tumor models has afforded more tunable and scrutable systems for patient-predictive platforms and mechanistic study. This review explores the technological innovations in 3D solid tumor models and their applicability to various aspects of cancer biology and identification of therapeutics. Features of the tumor and tumor microenvironment like spatial heterogeneity, multicellular populations and genomic variations are addressed and elaborated through the establishment of new in vitro models. We further address the integration of perfusable vasculature with 3D tumor models and the potentially wide-ranging applications of these more complex platforms in precision medicine and cancer immunotherapy. Finally, we provide an outlook on the future of experimental cancer models for both biological investigation and bench-to-bedside pipeline development.

Journal Article↗
PubMed2026

From family trials to genomic mate allocation: statistical and genomic strategies to accelerate sugarcane genetic improvement.

Sugarcane (Saccharum spp.) underpins global sugar and bioenergy supply and is increasingly valued as a renewable biomass feedstock. Sustained improvement in commercial traits and resilience is constrained by long breeding cycles, clonal propagation, multi-stage testing, and a highly polyploid, heterozygous, and frequently aneuploid genome with substantial non-additive genetic variation. Genomic selection has demonstrated value for predicting elite-clone performance, yet its operational use remains limited at earlier decision points, including family selection, parent evaluation, and cross design. This review examines the biological, statistical, and genomic factors that shape these decisions, with emphasis on the Australian breeding context based on progeny assessment trials (PATs), clonal assessment trials (CATs), and final assessment trials (FATs). We evaluate challenges arising from family plot means, the use of different full-sib samples as nominal family replicates, spatial heterogeneity, competition, genotype-by-environment interaction, and the partitioning of additive and non-additive effects. We also assess the integration of pedigree and genomic relationship, genotype representation, allele-dosage estimation, aneuploidy, genomic prediction models, and training-population design. We then consider genomic prediction of cross performance and constrained mate allocation as approaches for improving expected family performance, accounting for cross-specific non-additive effects and managing relatedness. We propose a decision-centred framework that links family and clonal data across breeding stages, tracks the propagation of information and uncertainty, and supports parent recycling and cross allocation. We conclude with a practical research agenda for stage-integrated mixed-model and single-step analyses that connect early family evaluation with genomic prediction and cross-level decision support in sugarcane breeding.

Saccharum↗
PubMed2025

Association of Lung Quantitative CT Scan Textures With Systemic Inflammation and Mortality in COPD.

BACKGROUND: COPD is characterized by persistent inflammation that is responsible for remodeling the bronchovascular bundles (BVBs), which may lead to poor quality of life. Quantitative CT (QCT) scan textures of the lung can capture local disease patterns of inflammation and related respiratory morbidity. RESEARCH QUESTION: Are BVB textures, obtained from the adaptive multiple feature method, associated with systemic inflammation, morbidity, and mortality in COPD? STUDY DESIGN AND METHODS: We analyzed data from the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS; n = 2,981) and the Genetic Epidemiology of COPD (COPDGene) study (n = 10,305). The predictors included 2 QCT scan biomarkers, the BVB and CT density gradient (CTDG) textures, age, sex, BMI, race, smoking status, pack-years of smoking, CT scan-detected emphysema, and square root of the wall area of a hypothetical airway with a 10-mm lumen perimeter (Pi10). Outcomes included plasma biomarker concentrations from Meso Scale Discovery proteomics assays and CBC counts, both as markers of inflammation, along with FEV1, FEV1 to FVC ratio, St. George's Respiratory Questionnaire score, 6-minute walk distance, and modified Medical Research Council dyspnea scale score. Associations of these QCT scan textures with FEV1 decline and all-cause mortality also were investigated. RESULTS: Increased BVB texture was associated significantly with elevated neutrophil and monocyte counts and the neutrophil to lymphocyte ratio, independent of clinical covariates, CT scan-detected emphysema, and Pi10. Elevated CTDG was associated with increased neutrophil count, NLR, and tumor necrosis factor &#x3b1;. Increased CTDG and BVB textures also were associated with a lower FEV1 and 6-minute walk distance. CTDG at baseline was also associated with decline in FEV1 at the 5-year follow-up in the COPDGene study. We observed a significant association of both BVB texture (SPIROMICS: hazard ratio [HR], 1.084 [95% CI, 1.035-1.135; P < .001]; COPDGene: HR, 1.106 [95% CI, 1.080-1.131; P < .001]) and CTDG texture (SPIROMICS: HR, 1.033 [95% CI, 1.003-1.064; P = .03]; COPDGene: HR, 1.079 [95% CI, 1.061-1.096; P < .001]) with all-cause mortality independent of CT scan-detected emphysema and Pi10. INTERPRETATION: QCT scan textures may provide imaging evidence of the spatial heterogeneity of lung inflammation and overall disease burden in COPD. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov; Nos.: NCT01969344 (SPIROMICS) and NCT00608764 (COPDGene); URL: www. CLINICALTRIALS: gov.

Humans↗
PubMed2026

Antibody-drug conjugates in selected solid tumours: a position statement update based on findings from the third workshop held by the ETOP IBCSG Partners Foundation.

The European Thoracic Oncology Platform (ETOP) International Breast Cancer Study Group (IBCSG) Partners Foundation initiated a series of workshops for experts to review current evidence and offer recommendations to guide future antibody-drug conjugate (ADC) research. Here, we summarise key findings from the third workshop, which included experts in various solid tumours, basic/translational research scientists and pharmaceutical industry representatives. Recent positive phase III trial data have further incorporated ADCs into the standard of care [e.g. lung: sacituzumab tirumotecan; breast: trastuzumab deruxtecan (T-DXd), sacituzumab govitecan, datopotamab deruxtecan; muscle-invasive bladder cancer: enfortumab vedotin; ovarian cancer: mirvetuximab soravastine; cervical cancer: tisotumab vedotin]. Thus, research priorities must be tailored according to tumour type, potentially focussing initially on settings where ADCs could replace chemotherapy. Many phase III ADC trials have been initiated based on positive phase I data and although these trials are larger than those conducted historically, prespecified criteria (e.g. patient numbers and magnitude of efficacy) should be met to justify proceeding directly to phase III. Importantly, although several ADCs have been successfully developed without mandatory biomarker selection, biomarker-driven ADC development enables rational patient selection, as illustrated by multiple ADCs (e.g. T-DXd, mirvetuximab soravtansine and telisotuzumab vedotin). The identification, development and validation of predictive biomarkers are therefore essential, particularly given several critical nuances, including the algorithms used to assess biomarker status and the type of specimen analysed, all of which may be influenced by temporal and spatial heterogeneity. Additional ADC research priorities include the optimisation of ADC constructs to enhance efficacy/tolerability and the identification of reliable ADC targets, including work to elucidate attributes of already-identified targets. Finally, considering the vast amount of ADC-related data being generated, artificial intelligence could be leveraged to analyse combined datasets and generate composite biomarkers, including tumour histology, optimal target expression thresholds, molecular alterations and activated pathways affecting payload activity and target function, to accelerate research.

Humans↗
PubMed2026

The genomic alchemist's arsenal: A comprehensive review of gene recruitment, regulatory rewiring, and the evolutionary arms race in snake envenomation.

Snake venom represents a striking example of evolutionary innovation, in which ancestral physiological gene networks have been co-opted into potent biochemical weapons. Advances in multi-omics, single-cell genomics, and structural bioinformatics have catalyzed a conceptual shift from descriptive toxin cataloging to a systems-level understanding of venom evolution, regulation, and function. This Review integrates genomic, cellular, and structural perspectives to delineate the molecular architecture underpinning venom diversification and target-site co-evolution. Emphasis is placed on regulatory mechanisms driving rapid expression plasticity, including super-enhancer activity, transposable element insertion, spatial heterogeneity within the venom gland, and non-coding RNA-mediated modulation. At the protein level, the review examines how hypervariable toxins engage in structural arms races with prey targets, and how multi-toxin complex formation, functional synergy, and molecular dynamics simulations inform models of lethality and resistance. A comparative framework is provided by contrasting high-potency predatory snake venoms with low-potency defensive venoms of hymenopterans such as bees and wasps, revealing how ecological selective pressures shape toxin potency, composition, and target specificity across taxa. Finally, current translational strategies are evaluated, with a focus on the relative merits of recombinant human monoclonal antibodies versus catalytic-site small-molecule inhibitors as deployable interventions for snakebite. By synthesizing evolutionary genomics, structural biology, comparative toxinology, and synthetic antivenomics, this Review outlines a predictive framework for anticipating venom evolutionary trajectories and for designing broad-spectrum, next-generation therapeutics.

Animals↗
PubMed1976

Enzyme variability in natural populations of Daphnia magna. IV. Ecological differentiation and frequency changes of genotypes at Audley End.

Genotypic frequencies were analysed for two years in a permanent population of the cladoceran crustacean, Daphnia magna, which was polymorphic for an esterase and for malate dehydrogenase. Large temporal changes in genotypic frequencies occurred at both loci. There was no evidence of a seasonal pattern in the frequency changes. In most samples, genotypes at the two enzyme loci were non-randomly associated; these associations showed temporal changes. On some occasions marked spatial heterogeneity in genotypic frequencies existed within the population. Genotypic differences in parthenogenetic and sexual egg production were observed. In a primarily parthenogenetically reproducing population, non-random associations between genotypes of structural and regulatory loci will be the rule. The allozyme variants themselves may or may not be under selection. The relevance of these observations to ecological studies on Daphnia is considered.

Animals↗
PubMed2026

Multiple local PfDHFR I164L haplotype expansions drive Plasmodium falciparum antifolate resistance in Uganda.

Mutations in the Plasmodium falciparum genes, pfdhfr and pfdhps, drive antifolate resistance and threaten malaria control in regions where sulfadoxine-pyrimethamine (SP) is the primary chemoprevention strategy. The spatial patterns and evolutionary dynamics of these mutations in high-transmission settings remain incompletely understood. Here we genotyped 11 resistance-associated mutations in pfdhfr and pfdhps in 4,725&#x2009;P. falciparum isolates collected from 16 Ugandan health facilities as part of annual surveillance between 2016 and 2022. Notably, we show that the frequency of PfDHFR I164L, which confers higher pyrimethamine resistance, increased over time from 19.4% to 32.4%. Using identity-by-descent, haplotype structure, and extended haplotype homozygosity analyses, we show that PfDHFR I164L is present on multiple haplotype backgrounds and undergoes localised expansions, without detectable signatures of recent positive selection at all but one site. Our results suggest that the evolution of antifolate resistance, driven by PfDHFR I164L, is spatially heterogeneous and complex in regions that primarily use SP chemoprevention programmes.

Plasmodium falciparum↗
PubMed2026

Signals of Natural Selection Across Regions of Low Recombination in Wild Populations of the Purple Sea Urchin, Strongylocentrotus purpuratus.

Structural variants (SVs) are increasingly recognized as important components of genetic architecture. Yet our understanding of the evolutionary forces maintaining SVs in natural populations is limited. Chromosomal inversions in particular can facilitate local adaptation in populations with high gene flow, including many marine species. The purple sea urchin (Strongylocentrotus purpuratus) is a powerful system to study these dynamics due to its high gene flow, lack of population structure, and broad latitudinal range. We analyzed whole genome sequence data from 137 individuals sampled across seven populations to identify regions of low recombination using scans for elevated linkage disequilibrium and genetic differentiation. Such regions may arise from structural variants, including chromosomal inversions. We identified nine regions showing signatures of reduced recombination, including three way genotype clustering, long range linkage, and hanging bridge patterns frequently associated with inversion polymorphisms. The regions were polymorphic within locations and along the species range with three loci showing concordant signatures of balancing and spatially heterogeneous selection based on enrichment of outliers and distinct patterns of allelic age. Additionally, these loci showed enrichment for genes associated with biomineralization and development. Our results provide the first evidence for regions of low recombination in the purple sea urchin genome, several of which display genomic signatures consistent with structural variants such as chromosomal inversions. These findings add to growing evidence that regions of reduced recombination constitute an important component of standing genetic variation in natural populations and may play a key role in adaptation to heterogeneous environments.

Strongylocentrotus purpuratus↗
PubMed1977

An implantable semiconductor beta-radiation detector.

An implantable beta-radiation detector suitable for the measurement of reginal blood flow in the experimental animal by the indicator clearance principle is described. A lithium-drifted silicon diode encapsulated in a stainless steel case is sutured over the site of interest. A suitable beta-emitting isotope, such as 85Kr in saline solution, is injected into the arterial supply and its calibrated against a mechanical system and showed excellent agreement up to 600 ml/100 g per min. At very high rates beyond the physiological range, flow was underestimated by a maximum of 10%. In vivo comparisons of myocardial blood flow assessed by the beta detector did not agree well with estimates of flow from a precordial counter or by the microsphere technique. Possible reasons are spatial heterogeneity of regional myocardial blood flow, the greatly different masses of tissue involved, or our inability to achieve sufficient numbers of spheres for accuracy in a 50-mg mass of tissue. The unit was still functional after 50 days in a chronic animal.

Animals↗
PubMed2026

IL1RAP Is Associated With an Inflammation-Immunity-Related State in Skin Cutaneous Melanoma: Integrative Evidence From Pan-Cancer Data and Melanoma Immunotherapy Cohorts.

BACKGROUND: The crosstalk between inflammation and immunity plays a central role in tumor progression, immune evasion, and therapeutic response. Interleukin-1 receptor accessory protein (IL1RAP) is a key adaptor in inflammatory signaling, yet its immunological relevance and clinical implications in skin cutaneous melanoma (SKCM) remain largely unexplored. METHODS: We performed an integrative analysis combining pan-cancer and melanoma-focused datasets. Bulk transcriptomic, single-cell, spatial transcriptomic, genomic alteration, pharmacogenomic, and clinical survival data were obtained from TCGA, GTEx, GEO, ENA, and other public resources. IL1RAP expression was evaluated across cancer types in relation to diagnostic performance, immune subtypes, survival outcomes, functional pathway activity, immune-genomic states, somatic alterations, and drug-response metrics. Melanoma-focused analyses examined immune infiltration, methylation-derived tumor-infiltrating lymphocyte (MeTIL) scores, and exploratory survival associations in five treatment cohorts; the survival groups were defined using cohort-specific optimal cutoffs rather than median splits. RESULTS: IL1RAP expression differed between tumor and normal tissues in multiple cancers, although the direction and magnitude varied by cancer type. Pan-cancer survival associations were likewise context dependent. Single-cell and spatial transcriptomic resources indicated cell-type and spatial heterogeneity of IL1RAP expression within tumor microenvironments. Pathway, immune-genomic, and pharmacogenomic analyses identified exploratory associations with functional states, genomic features, and drug-response metrics. In SKCM, IL1RAP expression was associated with several immune-infiltration estimates and higher MeTIL scores. Across five melanoma immunotherapy cohorts, the direction and magnitude of the overall survival associations varied substantially. CONCLUSIONS: This retrospective integrative analysis suggests that IL1RAP may mark an inflammation-immunity-related state in SKCM. The heterogeneous associations across cancers and melanoma treatment cohorts support further validation but do not establish IL1RAP as a causal regulator, a treatment-response predictor, or a therapeutic target.

IL1RAP↗
PubMed2026

Metagenomic insights into antibiotic resistance genes and virulence factors in sediments of river Yamuna.

Riverine sediments serve as critical reservoirs of microbial diversity and functional genes, reflecting both natural ecological processes and anthropogenic impacts. In the present study, we employed a shotgun metagenomic approach to investigate microbial community composition, antimicrobial resistance (AMR) genes, and virulence factors in sediments collected from three environmentally distinct locations of the Yamuna River near Agra, India, representing BSA, TGY, and YEA. The sediment DNA was subjected to high-throughput Illumina sequencing, followed by quality control, assembly, and open reading frame prediction. Taxonomic classification and diversity analyses were performed using MEGAN6 and R-based statistical tools, while AMR genes were identified from predicted metagenomic proteins using the Resistance Gene Identifier (RGI) against the CARD database, with high-confidence perfect and strict hits retained; ARGs were interpreted independently of species-level host assignment. Virulence factors were assessed through presence-absence profiling of functionally relevant gene categories. The results revealed pronounced spatial heterogeneity in microbial communities, with increasing taxonomic diversity, functional complexity, and evenness from BSA to TGY and YEA. TGY and YEA composite samples showed greater observed representation of high-confidence AMR gene predictions spanning multiple drug classes and resistance mechanisms, alongside a diverse repertoire of virulence-associated genes linked to motility, adhesion, and secretion systems. In contrast, the BSA site harbored a comparatively simpler resistome and virulome. Overall, this study highlights Yamuna River sediments as important reservoirs of resistance and virulence determinants and underscores the need for long-term genomic surveillance to inform risk assessment, pollution control, and sustainable river management strategies.

AMR↗
PubMed2026

Deep learning-based cross-attention fusion of multimodal MRI for survival prediction and risk stratification in IDH-wildtype glioblastoma: a multicenter study.

BACKGROUND: Glioblastoma (GBM) exhibits profound molecular and spatial heterogeneity, complicating prognostic evaluations. While multiparametric MRI provides crucial multidimensional biological information, conventional end-to-end deep learning integration strategies, such as early or late fusion, often fail to capture complex nonlinear cross-modal interactions. We aimed to systematically evaluate a cross-attention fusion (CAF) architecture for GBM survival prediction and quantify its incremental prognostic value relative to existing clinical tools. METHODS: In this multicenter retrospective study, 386 adults with IDH-wildtype, WHO grade 4 GBM were assembled from an institutional cohort (n = 226), the Chinese Glioma Genome Atlas (CGGA, n = 62), and The Cancer Genome Atlas (TCGA, n = 98). Using a unified 3D ResNet-18 backbone, we compared single-modality models, early fusion, late fusion, and CAF on preoperative T1-weighted, contrast-enhanced T1-weighted (T1CE), and T2-weighted MRI, and integrated the resulting deep learning risk score with routine clinical variables through multivariable Cox regression. Performance was assessed using Harrell's C-index, time-dependent AUC, and decision curve analysis. RESULTS: CAF showed numerically higher, more consistent C-index trends than early fusion, late fusion, and single-modality models (pooled C-index 0.629, 95% CI 0.594-0.664), although pairwise differences in time-dependent AUC were not statistically significant. Integrating clinical variables raised the pooled C-index to 0.691 (95% CI 0.660-0.721) in the treatment-era model, with comparable performance across the three cohorts (Local 0.688; CGGA 0.716; TCGA 0.689); a pre-treatment configuration excluding adjuvant therapy yielded a pooled C-index of 0.642. Under leave-one-cohort-out external validation, the combined model retained significant risk stratification in all held-out cohorts (C-index 0.63-0.71; all log-rank P&#xa0;<&#xa0;0.01), albeit with attenuated discrimination. The deep learning risk score remained independent after multivariable adjustment (HR 1.41 per SD, 95% CI 1.26-1.57; P&#xa0;<&#xa0;0.001). Kaplan-Meier analysis confirmed significant high- versus low-risk separation in all cohorts, and decision curve analysis showed greater net benefit than clinical-only and deep-learning-only models. CONCLUSION: The CAF-derived risk score offers prognostic information complementary to routine clinical variables, representing a promising noninvasive tool for individualized risk stratification when molecular profiling is incomplete or unavailable; these findings warrant prospective external validation before clinical use.

cross-attention fusion↗
PubMedDate not supplied

[Spike transmission in statistical neuronal ensembles. Induced epileptic focus in a model of hippocampal field CA3].

In a spatially heterogeneous model the transition to supercritical phase was investigated as to the parameter characterizing the activation level of the pyramidal cells related to one another assuming the nonuniformity radius to be R0 = const. This is a transition from spontaneous activity to epileptoid bursts. Before the onset of epileptoid bursts the region of stochastic nonequilibrium of solutions is developed likely to produce pathologic dynamic patterns. With further increase of the activation parameter the system comes to epileptoid state. This synchronized firing of pyramidal cells is accompanied with phases of inhibition. A decrease in the nonuniformity radius leads to the formation of an epileptic focus. It is a dissipative structure. The evolution of it is not further followed, since the transport equations do not include its dynamics.

Hippocampus↗
PubMed1976

Spatiotemporal organization of cat lateral geniculate receptive fields.

Spatial and temporal properties of LGN receptive fields were studied by flashing a small bar of light across the field in 28 discrete steps. The flashes at each of the spatial positions were used to produce 28 PST histograms. These histograms were in turn displayed as a plane, with space on the chi axis, time on the psi axis, and probability of firing on the zota axis. These response planes demonstrate that the terms on, off, center, and surround do not adequately describe when the simplest LGN receptive field. We, therefore, introduce a new terminology describing the four major spatiotemporal components of LGN fields. The primary excitatory (PE) domain corresponds to the strongest excitatory response, the secondary excitatory (SE) domain corresponds to the second-strongest excitatory domain, the primary inhibitory (PI) domain corresponds to the strongest inhibitory domain and, finally, the secondary inhibitory (SI) domain corresponds to the second-strongest inhibitory domain. Based on the arrangement of these four domains, it is possible to divide LGN fields into four major categories: 1) homogeneous-on, on-center receptive fields which have a spatially homogeneous distribution of domains; 2) homogeneous-off, off-center receptive fields which have a spatially homogeneous distribution of domains; 3) heterogeneous-on, on-center receptive fields which have a spatially heterogeneous distribution of domains; and 4) heterogeneous-off, off-center receptive fields which have a spatially heterogeneous distribution of domains; 3) heterogeneous-on, on-center receptive fields which have a spatially heterogeneous distribution of domains; and 4) heterogeneous-off, off-center receptive fields which have a spatially heterogeneous distribution of domains. Using grating, it can be demonstrated that our heterogeneous/homogeneous fields correspond to X/Y fields, respectively. These data lead us to suggest that retinal PE domains generage LGN PE and SI domains, while retinal SE domains generate LGN SE and SI domains.

Animals↗