Characterizing the effect of age and sex on macular retinal layer thicknesses and foveal pit morphology is crucial to differentiating between natural and disease-related changes. We applied advanced image analysis techniques to optical coherence tomography (OCT) to: 1) enhance the spatial description of age and sex effects, and 2) create a detailed open database of normative retinal layer thickness maps and foveal pit shapes. The maculae of 444 healthy subjects (age range 21–88) were imaged with OCT. Using computational spatial data analysis, thickness maps were obtained for retinal layers and averaged into 400 (20 x 20) sectors. Additionally, the geometry of the foveal pit was radially analyzed by computing the central foveal thickness, rim height, rim radius, and mean slope. The effect of age and sex on these parameters was analyzed with multiple regression mixed-effects models. We observed that the overall age-related decrease of the total retinal thickness (TRT) (-1.1% per 10 years) was mainly driven by the ganglion cell-inner plexiform layer (GCIPL) (-2.4% per 10 years). Both TRT and GCIPL thinning patterns were homogeneous across the macula when using percentual measurements. Although the male retina was 4.1 μm thicker on average, the greatest differences were mainly present for the inner retinal layers in the inner macular ring (up to 4% higher TRT than in the central macula). There was an age-related decrease in the rim height (1.0% per 10 years) and males had a higher rim height, shorter rim radius, and steeper mean slope. Importantly, the radial analysis revealed that these changes are present and relatively uniform across angular directions. These findings demonstrate the capacity of advanced analysis of OCT images to enhance the description of the macula. This, together with the created dataset, could aid the development of more accurate diagnosis models for macular pathologies.

Disentangling the cellular anatomy that gives rise to human visual perception is one of the main challenges of ophthalmology. Of particular interest is the foveal pit, a concave depression located at the center of the retina that captures light from the gaze center. In recent years, there has been a growing interest in studying the morphology of the foveal pit by extracting geometrical features from optical coherence tomography (OCT) images. Despite this, research has devoted little attention to comparing existing approaches for two key methodological steps: the location of the foveal center and the mathematical modelling of the foveal pit. Building upon a dataset of 185 healthy subjects imaged twice, in the present paper the image alignment accuracy of four different foveal center location methods is studied in the first place. Secondly, state-of-the-art foveal pit mathematical models are compared in terms of fitting error, repeatability, and bias. The results indicate the importance of using a robust foveal center location method to align images. Moreover, we show that foveal pit models can improve the agreement between different acquisition protocols. Nevertheless, they can also introduce important biases in the parameter estimates that should be considered.

Periodontitis, a ubiquitous severe gum disease affecting the teeth and surrounding alveolar bone, can heighten systemic inflammation. We investigated the association between very severe periodontitis and early biomarkers of age-related macular degeneration (AMD), in individuals with no eye disease. Cross-sectional analysis of the prospective community-based cohort United Kingdom (UK) Biobank. Sixty-seven thousand three hundred eleven UK residents aged 40 to 70 years recruited between 2006 and 2010 underwent retinal imaging. Macular-centered OCT images acquired at the baseline visit were segmented for retinal sublayer thicknesses. Very severe periodontitis was ascertained through a touchscreen questionnaire. Linear mixed effects regression modeled the association between very severe periodontitis and retinal sublayer thicknesses, adjusting for age, sex, ethnicity, socioeconomic status, alcohol consumption, smoking status, diabetes mellitus, hypertension, refractive error, and previous cataract surgery. Photoreceptor layer (PRL) and retinal pigment epithelium–Bruch’s membrane (RPE–BM) thicknesses. Among 36 897 participants included in the analysis, 1571 (4.3%) reported very severe periodontitis. Affected individuals were older, lived in areas of greater socioeconomic deprivation, and were more likely to be hypertensive, diabetic, and current smokers (all P < 0.001). On average, those with very severe periodontitis were hyperopic (0.05 ± 2.27 diopters) while those unaffected were myopic (−0.29 ± 2.40 diopters, P < 0.001). Following adjusted analysis, very severe periodontitis was associated with thinner PRL (−0.55 μm, 95% confidence interval [CI], −0.97 to −0.12; P = 0.022) but there was no difference in RPE–BM thickness (0.00 μm, 95% CI, −0.12 to 0.13; P = 0.97). The association between PRL thickness and very severe periodontitis was modified by age (P < 0.001). Stratifying individuals by age, thinner PRL was seen among those aged 60 to 69 years with disease (−1.19 μm, 95% CI, −1.85 to −0.53; P < 0.001) but not among those aged < 60 years. Among those with no known eye disease, very severe periodontitis is statistically associated with a thinner PRL, consistent with incipient AMD. Optimizing oral hygiene may hold additional relevance for people at risk of degenerative retinal disease. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Recent advances in broadband communications have led to the first standardization phase of 5G (Rel’ 15). Several works present this novel standard as the main solution in order to cover all the new requirements that have arisen in the multimedia delivery industry. However, the latest 5G release does not include the option of transmitting traditional broadcast services. That is why, in this paper, Non-Orthogonal Multiple Access (NOMA) is presented as a promising candidate in order to boost the implementation of the broadcast/multicast communications within the 5G NR standard. Therefore, a 5G New Radio (NR) fully compliant simulation tool has been developed and NOMA multiplexing schemes have been added, introducing as few modifications as possible. The simulation platform and proposed techniques have been tested within two different use cases. Results present a better overall reliability performance of the communications by using NOMA. In fact, both services performance is improved when compared to traditional TDMA schemes, with a maximum gain around 4 dB.

Tractometry analysis represents a significant advancement in neuroimaging, offering a detailed examination of the brain's white matter at a micro level. Unlike traditional ROI or voxel-based methods, tractometry precisely reconstructs and characterizes white matter tracts. Using advanced diffusion MRI and tractography algorithms, it maps the trajectory, shape, and connectivity patterns of individual white matter bundles. Accurate alignment of these tracts across different groups is crucial for reliable and reproducible results. Nonlinear registration techniques are essential for achieving this alignment, harmonizing bundle shapes, and improving sensitivity to disease-related changes. However, nonlinear registration is complex, especially with tractography data, which digitally represents the brain's white matter anatomy. Potential structural changes in the bundle's shape during registration can lead to artifacts that obscure critical anatomical details needed for disease identification. We introduce BundleWarp, a streamline-based nonlinear deformable registration method designed specifically for white matter tracts. BundleWarp employs a sophisticated approach to align two white matter bundles while preserving their topological and anatomical features. It is formulated as a probability density estimation problem with motion coherence penalties, ensuring coherent movement of points along streamlines and maintaining the anatomical integrity of tracts through displacement field regularization. Additionally, we introduce a tract morphometry framework utilizing the displacement field generated by BundleWarp to analyze white matter tract shape differences. Our results show that BundleWarp effectively quantifies bundle shape differences and enhances structural harmonization in tractometry analysis for diverse subjects, including those with Alzheimer's and Parkinson's disease. Test-retest experiments further demonstrate that BundleWarp substantially improves subject fingerprinting by increasing within-subject reproducibility of both bundle shape and microstructural profiles (FA, MD, RD, AD). It precisely maps the brain's neuronal pathways, offering a robust tractometry framework with enhanced sensitivity for detecting disease-related structural and microstructural changes in white matter tracts associated with Mild Cognitive Impairment (MCI), dementia, and early-stage Alzheimer's biomarkers, including amyloid-beta plaques and tau neurofibrillary tangles.

Local content insertion and viewer segmentation with proximity service information is a critical business case for broadcasting operators. This personalized service delivery will open up an opportunity for new use cases, such as local service delivery, local advertisement insertion and local emergency warning. In practice, single frequency networks (SFNs) offer a better spectrum efficiency when compared with traditional Multiple Frequency Network (MFN) approaches. Nevertheless, the addition of local content delivery can dramatically reduce that efficiency. Recently, the Layered Division Multiplexing (LDM) based Local Service Insertion (LSI) solution has been proposed as an interesting alternative for including local content in single frequency networks. Nonetheless, the required channel estimation procedure has been considered a major drawback for its successful implementation. In this work, a new semi-blind channel estimation algorithm is proposed. The algorithm takes advantage of the LDM structure and it is based on the statistical properties of the time domain Orthogonal Frequency Division Multiplexing (OFDM) frame. It proposes a solution for the major challenges that have remain unsolved so far. The paper also includes the first simulation results for this new approach.