Compartmentalized ocular lymphatic system mediates eye-brain immunity.

The eye, an anatomical extension of the central nervous system (CNS), exhibits many molecular and cellular parallels to the brain. Emerging research demonstrates that changes in the brain are often reflected in the eye, particularly in the retina1. Still, the possibility of an immunological nexus between the posterior eye and the rest of the CNS tissues remains unexplored. Here, studying immune responses to herpes simplex virus in the brain, we observed that intravitreal immunization protects mice against intracranial viral challenge. This protection extended to bacteria and even tumours, allowing therapeutic immune responses against glioblastoma through intravitreal immunization. We further show that the anterior and posterior compartments of the eye have distinct lymphatic drainage systems, with the latter draining to the deep cervical lymph nodes through lymphatic vasculature in the optic nerve sheath. This posterior lymphatic drainage, like that of meningeal lymphatics, could be modulated by the lymphatic stimulator VEGFC. Conversely, we show that inhibition of lymphatic signalling on the optic nerve could overcome a major limitation in gene therapy by diminishing the immune response to adeno-associated virus and ensuring continued efficacy after multiple doses. These results reveal a shared lymphatic circuit able to mount a unified immune response between the posterior eye and the brain, highlighting an understudied immunological feature of the eye and opening up the potential for new therapeutic strategies in ocular and CNS diseases.

VEGF-C-driven lymphatic drainage enables immunosurveillance of brain tumours.

Immune surveillance against pathogens and tumours in the central nervous system is thought to be limited owing to the lack of lymphatic drainage. However, the characterization of the meningeal lymphatic network has shed light on previously unappreciated ways that an immune response can be elicited to antigens that are expressed in the brain1-3. Despite progress in our understanding of the development and structure of the meningeal lymphatic system, the contribution of this network in evoking a protective antigen-specific immune response in the brain remains unclear. Here, using a mouse model of glioblastoma, we show that the meningeal lymphatic vasculature can be manipulated to mount better immune responses against brain tumours. The immunity that is mediated by CD8 T cells to the glioblastoma antigen is very limited when the tumour is confined to the central nervous system, resulting in uncontrolled tumour growth. However, ectopic expression of vascular endothelial growth factor C (VEGF-C) promotes enhanced priming of CD8 T cells in the draining deep cervical lymph nodes, migration of CD8 T cells into the tumour, rapid clearance of the glioblastoma and a long-lasting antitumour memory response. Furthermore, transfection of an mRNA construct that expresses VEGF-C works synergistically with checkpoint blockade therapy to eradicate existing glioblastoma. These results reveal the capacity of VEGF-C to promote immune surveillance of tumours, and suggest a new therapeutic approach to treat brain tumours.

Improving RGB illuminant estimation exploiting spectral average radiance.

We introduce a method that enhances RGB color constancy accuracy by combining neural network and k-means clustering techniques. Our approach stands out from previous works because we combine multispectral and color information together to estimate illuminants. Furthermore, we investigate the combination of the illuminant estimation in the RGB color and in the spectral domains, as a strategy to provide a refined estimation in the RGB color domain. Our investigation can be divided into three main points: (1) identify the spatial resolution for sampling the input image in terms of RGB color and spectral information that brings the highest performance; (2) determine whether it is more effective to predict the illuminant in the spectral or in the RGB color domain, and finally, (3) assuming that the illuminant is in fact predicted in the spectral domain, investigate if it is better to have a loss function defined in the RGB color or spectral domain. Experimental results are carried out on NUS: a standard dataset of multispectral radiance images with an annotated spectral global illuminant. Among the several considered options, the best results are obtained with a model trained to predict the illuminant in the spectral domain using an RGB color loss function. In terms of comparison with the state of the art, this solution improves the recovery angular error metric by 66% compared to the best tested spectral method, and by 41% compared to the best tested RGB method.

CCL21-CCR7 signaling promotes microglia/macrophage recruitment and chemotherapy resistance in glioblastoma.

Glioblastoma (GBM) is the most common and fatal primary tumor of the central nervous system (CNS) and current treatments have limited success. Chemokine signaling regulates both malignant cells and stromal cells of the tumor microenvironment (TME), constituting a potential therapeutic target against brain cancers. Here, we investigated the C-C chemokine receptor type 7 (CCR7) and the chemokine (C-C-motif) ligand 21 (CCL21) for their expression and function in human GBM and then assessed their therapeutic potential in preclinical mouse GBM models. In GBM patients, CCR7 expression positively associated with a poor survival. CCL21-CCR7 signaling was shown to regulate tumor cell migration and proliferation while also controlling tumor associated microglia/macrophage recruitment and VEGF-A production, thereby controlling vascular dysmorphia. Inhibition of CCL21-CCR7 signaling led to an increased sensitivity to temozolomide-induced tumor cell death. Collectively, our data indicate that drug targeting of CCL21-CCR7 signaling in tumor and TME cells is a therapeutic option against GBM.

Raw Spectral Filter Array Imaging for Scene Recognition.

Scene recognition is the task of identifying the environment shown in an image. Spectral filter array cameras allow for fast capture of multispectral images. Scene recognition in multispectral images is usually performed after demosaicing the raw image. Along with adding latency, this makes the classification algorithm limited by the artifacts produced by the demosaicing process. This work explores scene recognition performed on raw spectral filter array images using convolutional neural networks. For this purpose, a new raw image dataset is collected for scene recognition with a spectral filter array camera. The classification is performed using a model constructed based on the pretrained Places-CNN. This model utilizes all nine channels of spectral information in the images. A label mapping scheme is also applied to classify the new dataset. Experiments are conducted with different pre-processing steps applied on the raw images and the results are compared. Higher-resolution images are found to perform better even if they contain mosaic patterns.

Spectral Reconstruction from RGB Imagery: A Potential Option for Infinite Spectral Data?

Spectral imaging has revolutionisedvarious fields by capturing detailed spatial and spectral information. However, its high cost and complexity limit the acquisition of a large amount of data to generalise processes and methods, thus limiting widespread adoption. To overcome this issue, a body of the literature investigates how to reconstruct spectral information from RGB images, with recent methods reaching a fairly low error of reconstruction, as demonstrated in the recent literature. This article explores the modification of information in the case of RGB-to-spectral reconstruction beyond reconstruction metrics, with a focus on assessing the accuracy of the reconstruction process and its ability to replicate full spectral information. In addition to this, we conduct a colorimetric relighting analysis based on the reconstructed spectra. We investigate the information representation by principal component analysis and demonstrate that, while the reconstruction error of the state-of-the-art reconstruction method is low, the nature of the reconstructed information is different. While it appears that the use in colour imaging comes with very good performance to handle illumination, the distribution of information difference between the measured and estimated spectra suggests that caution should be exercised before generalising the use of this approach.

Logistic splicing correction for VNIR-SWIR reflectance imaging spectroscopy.

In the field of spectroscopy, a splicing correction is a process by which two spectra captured with different sensors in adjacent or overlapping electromagnetic spectrum ranges are smoothly connected. In our study, we extend this concept to the case of reflectance imaging spectroscopy in the visible-near-infrared (VNIR) and short-wave infrared (SWIR), accounting for additional sources of noise that arise at the pixel level. The proposed approach exploits the adaptive fitting of a logistic function to compute correcting coefficients that harmonize the two spectral sets. This short Letter addresses usage conditions and compares results against the existing state of the art.

Filtered Lebedev quadrature method for robust and efficient beam shape coefficient estimation in acoustic tweezers calibration.

Acoustic tweezers offer a contactless, three-dimensional, and selective approach to trapping objects by harnessing the acoustic radiation force. Precise control of this technique requires accurate calibration of the force, which depends on the object's properties and the spherical harmonics expansion of the incident field through the beam shape coefficients. Previous studies showed that these coefficients can be determined using either the Lebedev quadrature or the angular spectrum methods. However, the former is highly susceptible to noise, while the latter demands extensive implementation time due to the number of required measurement points. A filtered method with a reduced number of points is introduced to address these limitations. Initially, we emphasize the implicit filtering in the angular spectrum method, allowing relative noise insensitivity. Subsequently, we present its unfiltered version, enabling force estimation of a standing field. Finally, we develop a filtered method based on the Lebedev quadrature, requiring fewer points, and apply it to focused vortex beams. Numerical evaluation of the radiation force demonstrates the method's resilience to noise and a reduced need for points compared to previous methods. The filtered Lebedev method paves the way for characterizing high-frequency acoustic tweezers, where measurement constraints necessitate rapid and robust beam shape coefficient estimation techniques.

Measuring the Optical Properties of Highly Diffuse Materials.

Measuring the optical properties of highly diffuse materials is a challenge as it could be related to the white colour or an oversaturation of pixels in the acquisition system. We used a spatially resolved method and adapted a nonlinear trust-region algorithm to the fit Farrell diffusion theory model. We established an inversion method to estimate two optical properties of a material through a single reflectance measurement: the absorption and the reduced scattering coefficient. We demonstrate the validity of our method by comparing results obtained on milk samples, with a good fitting and a retrieval of linear correlations with the fat content, given by R2 scores over 0.94 with low p-values. The values of absorption coefficients retrieved vary between 1 × 10-3 and 8 × 10-3 mm-1, whilst the values of the scattering coefficients obtained from our method are between 3 and 8 mm-1 depending on the percentage of fat in the milk sample, and under the assumption of the anisotropy factor g>0.8. We also measured and analyzed the results on white paint and paper, although the paper results were difficult to relate to indicators. Thus, the method designed works for highly diffuse isotropic materials.

Impact of Exposure and Illumination on Texture Classification Based on Raw Spectral Filter Array Images.

Spectral Filter Array cameras provide a fast and portable solution for spectral imaging. Texture classification from images captured with such a camera usually happens after a demosaicing process, which makes the classification performance rely on the quality of the demosaicing. This work investigates texture classification methods applied directly to the raw image. We trained a Convolutional Neural Network and compared its classification performance to the Local Binary Pattern method. The experiment is based on real SFA images of the objects of the HyTexiLa database and not on simulated data as are often used. We also investigate the role of integration time and illumination on the performance of the classification methods. The Convolutional Neural Network outperforms other texture classification methods even with a small amount of training data. Additionally, we demonstrated the model's ability to adapt and scale for different environmental conditions such as illumination and exposure compared to other methods. In order to explain these results, we analyze the extracted features of our method and show the ability of the model to recognize different shapes, patterns, and marks in different textures.

Tannin phenotyping of the Vitaceae reveals a phylogenetic linkage of epigallocatechin in berries and leaves.

BACKGROUND AND AIMS: Condensed tannins, responsible for berry and wine astringency, may have been selected during grapevine domestication. This work examines the phylogenetic distribution of condensed tannins throughout the Vitaceae phylogenetic tree. METHODS: Green berries and mature leaves of representative true-to-type members of the Vitaceae were collected before 'véraison', freeze-dried and pulverized, and condensed tannins were measured following depolymerization by nucleophilic addition of 2-mercaptoethanol to the C4 of the flavan-3-ol units in an organic acidic medium. Reaction products were separated and quantified by ultrahigh pressure liquid chromatography/diode array detection/mass spectrometry. KEY RESULTS AND CONCLUSIONS: The original ability to incorporate epigallocatechin (EGC) into grapevine condensed tannins was lost independently in both the American and Eurasian/Asian branches of the Vitaceae, with exceptional cases of reversion to the ancestral EGC phenotype. This is particularly true in the genus Vitis, where we now find two radically distinct groups differing with respect to EGC content. While Vitis species from Asia are void of EGC, 50 % of the New World Vitis harbour EGC. Interestingly, the presence of EGC is tightly coupled with the degree of leaf margin serration. Noticeably, the rare Asian EGC-forming species are phylogenetically close to Vitis vinifera, the only remnant representative of Vitis in Eurasia. Both the wild ancestral V. vinifera subsp. sylvestris as well as the domesticated V. vinifera subsp. sativa can accumulate EGC and activate galloylation biosynthesis that compete for photoassimilates and reductive power.

Modification and evaluation of the peripheral contrast sensitivity function models.

We propose a series of modifications to the Barten contrast sensitivity function model for peripheral vision based on anatomical and psychophysical studies. These modifications result in a luminance pattern detection model that could quantitatively describe the extent of veridical pattern resolution and the aliasing zone. We evaluated our model against psychophysical measurements in peripheral vision. Our numerical assessment shows that the modified Barten leads to lower estimate errors than its original version.

Image quality and perception: introduction.

This feature issue focuses on image quality and perception, including image and video quality, subjective and objective quality, and enhancement. The feature issue contains papers on several important topics, such as contrast discrimination, analysis of color imaging in cameras, image quality assessment, and more. The papers represent different important aspects in image quality and perception, contributing to the advancement of the field.

Acoustic Estimation of the Direction of Arrival of an Unmanned Aerial Vehicle Based on Frequency Tracking in the Time-Frequency Plane.

The development of unmanned aerial vehicles (UAVs) opens up a lot of opportunities but also brings some threats. Dealing with these threats is not easy and requires some good techniques. Knowing the location of the threat is essential to deal with an UAV that is displaying disturbing behavior. Many methods exist but can be very limited due to the size of UAVs or due to technological improvements over the years. However, the noise produced by the UAVs is still predominant, so it gives a good opening for the development of acoustic methods. The method presented here takes advantage of a microphone array with a processing based on time domain Delay and Sum Beamforming. In order to obtain a better signal to noise ratio, the UAV's acoustic signature is taken into account in the processing by using a time-frequency representation of the beamformer's output. Then, only the content related to this signature is considered to calculate the energy in one direction. This method enables to have a good robustness to noise and to localize an UAV with a poor spectral content or to separate two UAVs with different spectral contents. Simulation results and those of a real flight experiment are reported.

A System for Autonomous Seaweed Farm Inspection with an Underwater Robot.

This paper outlines challenges and opportunities in operating underwater robots (so-called AUVs) on a seaweed farm. The need is driven by an emerging aquaculture industry on the Swedish west coast where large-scale seaweed farms are being developed. In this paper, the operational challenges are described and key technologies in using autonomous systems as a core part of the operation are developed and demonstrated. The paper presents a system and methods for operating an AUV in the seaweed farm, including initial localization of the farm based on a prior estimate and dead-reckoning navigation, and the subsequent scanning of the entire farm. Critical data from sidescan sonars for algorithm development are collected from real environments at a test site in the ocean, and the results are demonstrated in a simulated seaweed farm setup.

Highly disordered histone H1-DNA model complexes and their condensates.

Disordered proteins play an essential role in a wide variety of biological processes, and are often posttranslationally modified. One such protein is histone H1; its highly disordered C-terminal tail (CH1) condenses internucleosomal linker DNA in chromatin in a way that is still poorly understood. Moreover, CH1 is phosphorylated in a cell cycle-dependent manner that correlates with changes in the chromatin condensation level. Here we present a model system that recapitulates key aspects of the in vivo process, and also allows a detailed structural and biophysical analysis of the stages before and after condensation. CH1 remains disordered in the DNA-bound state, despite its nanomolar affinity. Phase-separated droplets (coacervates) form, containing higher-order assemblies of CH1/DNA complexes. Phosphorylation at three serine residues, spaced along the length of the tail, has little effect on the local properties of the condensate. However, it dramatically alters higher-order structure in the coacervate and reduces partitioning to the coacervate phase. These observations show that disordered proteins can bind tightly to DNA without a disorder-to-order transition. Importantly, they also provide mechanistic insights into how higher-order structures can be exquisitely sensitive to perturbation by posttranslational modifications, thus broadening the repertoire of mechanisms that might regulate chromatin and other macromolecular assemblies.

Oligodendrocyte precursor survival and differentiation requires chromatin remodeling by Chd7 and Chd8.

Oligodendrocyte precursor cells (OPCs) constitute the main proliferative cells in the adult brain, and deregulation of OPC proliferation-differentiation balance results in either glioma formation or defective adaptive (re)myelination. OPC differentiation requires significant genetic reprogramming, implicating chromatin remodeling. Mounting evidence indicates that chromatin remodelers play important roles during normal development and their mutations are associated with neurodevelopmental defects, with CHD7 haploinsuficiency being the cause of CHARGE syndrome and CHD8 being one of the strongest autism spectrum disorder (ASD) high-risk-associated genes. Herein, we report on uncharacterized functions of the chromatin remodelers Chd7 and Chd8 in OPCs. Their OPC-chromatin binding profile, combined with transcriptome and chromatin accessibility analyses of Chd7-deleted OPCs, demonstrates that Chd7 protects nonproliferative OPCs from apoptosis by chromatin closing and transcriptional repression of p53 Furthermore, Chd7 controls OPC differentiation through chromatin opening and transcriptional activation of key regulators, including Sox10, Nkx2.2, and Gpr17 However, Chd7 is dispensable for oligodendrocyte stage progression, consistent with Chd8 compensatory function, as suggested by their common chromatin-binding profiles and genetic interaction. Finally, CHD7 and CHD8 bind in OPCs to a majority of ASD risk-associated genes, suggesting an implication of oligodendrocyte lineage cells in ASD neurological defects. Our results thus offer new avenues to understand and modulate the CHD7 and CHD8 functions in normal development and disease.

Translucency perception: A review.

Translucency is an optical and a perceptual phenomenon that characterizes subsurface light transport through objects and materials. Translucency as an optical property of a material relates to the radiative transfer inside and through this medium, and translucency as a perceptual phenomenon describes the visual sensation experienced by humans when observing a given material under given conditions. The knowledge about the visual mechanisms of the translucency perception remains limited. Accurate prediction of the appearance of the translucent objects can have a significant commercial impact in the fields such as three-dimensional printing. However, little is known how the optical properties of a material relate to a perception evoked in humans. This article overviews the knowledge status about the visual perception of translucency and highlights the applications of the translucency perception research. Furthermore, this review summarizes current knowledge gaps, fundamental challenges and existing ambiguities with a goal to facilitate translucency perception research in the future.