Natural low-illumination image enhancement based on dual-channel prior information.

This paper proposes an adaptive image enhancement method that aims to effectively restore the brightness, detail, and natural color of various low-illumination images. To be specific, the method first constructs the initial dual-channel illumination map of the image. Next, the optimal illumination correction coefficient is calculated by the prior information entropy of the initial illumination map, which helps to correct potentially erroneous illumination estimates. To restore the illumination, gamma correction is used with the optimal illumination correction coefficient. Finally, an improved perfect reflection constraint model is used to restore the color of the image. Both visual analysis and quantitative comparison with state-of-the-art methods demonstrate the effectiveness of the method in terms of brightness adjustment, detail recovery, and color restoration.

Challenges in illumination analysis and design for Martian surface-level habitation.

It is needless to say that travel to and settlement on Mars are associated with extreme levels of scientific and engineering issues. This will only be amplified with the long-term duration of the mission, not only due to scarcity of resources, but also as the psychological aspects of the dynamics among the crew increase drastically. It should be emphasized that this is a scientific crew, who have undergone high levels of confinement during space travel to Mars, O (102 Earth days), are living in semi-solitude and partial confinement conditions for durations of O (103 Earth days), and even at the nominal termination of the mission, foresee a high-risk and arduous travel time of O (102 Earth days) back to the Earth. The mental weight of the described mission with its slow pace and tardy episodes, puts the crew under severe psychological issues. Minimal and conservative design of spaces, lack of constant access to the exterior, and social solitude are among major factors contributing to the psychological well-being of the crew. Furthermore, the overall lower levels of natural light, accompanied by the minimum possible area of transparent facades, protecting the crew from harmful radiations and cold exterior, burden the mental conditions of the crew even more. Given the limited availability of data from the surface of Mars, study of the effects linked to the lighting and illumination design of the habitats is challenging. The current manuscript hopes to shed light on the illumination and lighting design and simulation procedure, required data, assumptions, and final results for the surface-level habitats on Mars.•Mars / Sub orbital configuration allows for limited natural lighting, however, upon site-specific analysis, it might be considerable as a base passive source.•Current simulation tools are design based on Earth-bound design requirements. These need to be re-oriented to match available planetary data.

Target Recognition Based on Infrared and Visible Image Fusion and Improved YOLOv8 Algorithm.

In response to the issue that the fusion process of infrared and visible images is easily affected by lighting factors, in this paper, we propose an adaptive illumination perception fusion mechanism, which was integrated into an infrared and visible image fusion network. Spatial attention mechanisms were applied to both infrared images and visible images for feature extraction. Deep convolutional neural networks were utilized for further feature information extraction. The adaptive illumination perception fusion mechanism is then integrated into the image reconstruction process to reduce the impact of lighting variations in the fused images. A Median Strengthening Channel and Spatial Attention Module (MSCS) was designed to be integrated into the backbone of YOLOv8. In this paper, we used the fusion network to create a dataset named ivifdata for training the target recognition network. The experimental results indicated that the improved YOLOv8 network saw further enhancements of 2.3%, 1.4%, and 8.2% in the Recall, mAP50, and mAP50-95 metrics, respectively. The experiments revealed that the improved YOLOv8 network has advantages in terms of recognition rate and completeness, while also reducing the rates of false negatives and false positives.

Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signaling in a Model of Niemann-Pick Disease Type C.

Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing understanding of the basis of neuronal functions. However, the rapid millisecond-scale fluctuations in neuronal activity and the subtle sub-diffraction resolution changes of synaptic morphology pose significant challenges to this endeavor. Here, specially designed graphene microelectrode arrays (G-MEAs) are used, which are compatible with high spatial resolution imaging across various scales as well as permit high temporal resolution electrophysiological recordings to address these challenges. Furthermore, alongside G-MEAs, an easy-to-implement machine learning algorithm is developed to efficiently process the large datasets collected from MEA recordings. It is demonstrated that the combined use of G-MEAs, machine learning (ML) spike analysis, and 4D structured illumination microscopy (SIM) enables monitoring the impact of disease progression on hippocampal neurons which are treated with an intracellular cholesterol transport inhibitor mimicking Niemann-Pick disease type C (NPC), and show that synaptic boutons, compared to untreated controls, significantly increase in size, leading to a loss in neuronal signaling capacity.

Localized Plasmonic Structured Illumination Microscopy Using Hybrid Inverse Design.

Super-resolution fluorescence imaging has offered unprecedented insights and revolutionized our understanding of biology. In particular, localized plasmonic structured illumination microscopy (LPSIM) achieves video-rate super-resolution imaging with ∼50 nm spatial resolution by leveraging subdiffraction-limited nearfield patterns generated by plasmonic nanoantenna arrays. However, the conventional trial-and-error design process for LPSIM arrays is time-consuming and computationally intensive, limiting the exploration of optimal designs. Here, we propose a hybrid inverse design framework combining deep learning and genetic algorithms to refine LPSIM arrays. A population of designs is evaluated using a trained convolutional neural network, and a multiobjective optimization method optimizes them through iteration and evolution. Simulations demonstrate that the optimized LPSIM substrate surpasses traditional substrates, exhibiting higher reconstruction accuracy, robustness against noise, and increased tolerance for fewer measurements. This framework not only proves the efficacy of inverse design for tailoring LPSIM substrates but also opens avenues for exploring new plasmonic nanostructures in imaging applications.

Visualizing highly bright and uniform cellular ultrastructure by expansion-microscopy with tetrahedral DNA nanostructures.

Expansion Microscopy (ExM) is a widely used super-resolution technique that enables imaging of structures beyond the diffraction limit of light. However, ExM suffers from weak labeling signals and expansion distortions, limiting its applicability. Here, we present an innovative approach called Tetrahedral DNA nanostructure Expansion Microscopy (TDN-ExM), addressing these limitations by using tetrahedral DNA nanostructures (TDNs) for fluorescence labeling. Our approach demonstrates a 3- to 10-fold signal amplification due to the multivertex nature of TDNs, allowing the modification of multiple dyes. Previous studies have confirmed minimal distortion on a large scale, and our strategy can reduce the distortion at the ultrastructural level in samples because it does not rely on anchoring agents and is not affected by digestion. This results in a brighter fluorescence, better uniformity, and compatibility with different labeling strategies and optical super-resolution technologies. We validated the utility of TDN-ExM by imaging various biological structures with improved resolutions and signal-to-noise ratios.

In vivo biosensing of subcellular pyruvate pools reveals photosynthesis-dependent metabolite dynamics in Nicotiana benthamiana.

Pyruvate is central to metabolism across biology. It acts as a metabolic hub linking key pathways including glycolysis, the Krebs cycle, fermentation, and synthesis of amino acids, fatty acids, isoprenoids and nucleotides. Even though the central role of pyruvate is well established biochemically, there is a remarkable void in our understanding of how pyruvate levels behave within cells, where pyruvate is distributed across different compartments, and differential changes in pyruvate pools may occur rapidly upon changes in metabolic fluxes. Recently, this problem has been addressed by the development of a genetically-encoded pyruvate biosensor to provide first insights into the pyruvate dynamics in animal cells. Here, we establish in vivo biosensing of pyruvate in plants. We provide advanced characterisation of the biosensor properties and demonstrate the functionality of the sensor in the cytosol, the mitochondria and the chloroplasts of Nicotiana benthamiana epidermal cells. Finally, we harnessed the tool to investigate the impact of photosynthesis on pyruvate with unprecedented spatial and temporal resolution, revealing pronounced changes in cytosolic pyruvate pools. While highlighting the current limitations of the biosensor, this study provides proof-of-concept for how the dynamics and regulation of central carbon metabolites can be revealed in the context of living plant tissues.

Exploring effects of light intensity on sustainability indicators in finishing pig production.

With an ongoing transition towards the use of Light Emitting Diodes, more knowledge is needed on which light settings optimise sustainability parameters in pig production. We studied the effects of four light intensities on social, environmental and economic sustainability indicators, including ammonia emissions, space use, pen fouling, weight gain, carcass quality, perception of the stockkeeper, costs of the light system, and use of drinking water, electricity and medicines. Light treatments included a low (45 lux), medium (198 lux) and high (968 lux) uniform intensity, and a spatial gradient treatment ranging from 71 lux in the front to 330 lux in the back of each pen. The latter treatment aimed to improve the space use of functional areas. A total of 448 growing-finishing pigs were studied on a commercial farm using two consecutive batches of four rooms containing eight pens with seven pigs. Light intensity influenced some aspects of space use and pen fouling. For example, the proportion of pigs lying in the resting area was higher in the high and medium light intensity treatment than in the low intensity and gradient treatment. Moreover, the high-intensity treatment resulted in more fouling with faeces in the feeding area compared with the low-intensity and the gradient treatment. Ammonia emissions were higher in the gradient than in the low intensity treatment (not measured in medium and high intensity treatment). Furthermore, light intensity did not affect weight gain, carcass quality, water use and medicine use. The stockkeeper was content to work in all light conditions, but slightly preferred the medium intensity due to optimal visibility. Concerning economic performance, the costs of the light system and electricity use increased in the following order: low intensity, gradient, medium intensity, and high intensity. In conclusion, contrary to expectation the spatial gradient did not notably improve space use or reduce pen fouling, but rather increased ammonia emissions in comparison with uniform light. This is likely because the gradient could not be applied in an optimal way in the existing housing conditions. Among the other sustainability indicators, mainly electricity use and costs of the light system differed per treatment. These aspects can be improved by further optimising the number of light sources needed per pen to achieve the targeted intensities.

Mirror-assisted light-sheet microscopy: a simple upgrade to enable bi-directional sample excitation.

Significance: Light-sheet microscopy is a powerful imaging technique that achieves optical sectioning via selective illumination of individual sample planes. However, when the sample contains opaque or scattering tissues, the incident light sheet may not be able to uniformly excite the entire sample. For example, in the context of larval zebrafish whole-brain imaging, occlusion by the eyes prevents access to a significant portion of the brain during common implementations using unidirectional illumination. Aim: We introduce mirror-assisted light-sheet microscopy (mLSM), a simple inexpensive method that can be implemented on existing digitally scanned light-sheet setups by adding a single optical element-a mirrored micro-prism. The prism is placed near the sample to generate a second excitation path for accessing previously obstructed regions. Approach: Scanning the laser beam onto the mirror generates a second, reflected illumination path that circumvents the occlusion. Online tuning of the scanning and laser power waveforms enables near uniform sample excitation with dual illumination. Results: mLSM produces cellular-resolution images of zebrafish brain regions inaccessible to unidirectional illumination. The imaging quality in regions illuminated by the direct or reflected sheet is adjustable by translating the excitation objective. The prism does not interfere with visually guided behavior. Conclusions: mLSM presents an easy-to-implement, cost-effective way to upgrade an existing light-sheet system to obtain more imaging data from a biological sample.

Synergistic optimization of baffles and aeration to improve the Light/Dark cycle of microalgae photobioreactor for enhanced nitrogen removal performance: Computational fluid dynamics and experimental verification.

Microalgae photobioreactor (PBR) is a kind of efficient wastewater treatment system for nitrogen removal. However, there is still an urgent need for process optimization of PBR. Especially, the synergistic effect and optimization of light and flow state poses a challenge. In this study, the computational fluid dynamics is employed for simulating the optimization of the number and length of the internal baffles, as well as the aeration rate of PBR, which in turn leads to the optimal growth of microalgae and efficient nitrogen removal. After optimization, the Light/Dark cycle of the reactor B is shortened by 51.6 %, and the biomass increases from 0.06 g/L to 3.94 g/L. In addition, the removal rate of NH4+-N increased by 106.0 % to 1.56 mg L-1 h-1. This work provides a feasible method for optimizing the design and operational parameters of PBR aiming the engineering application.

Far-Field Phase-Shifting Structured Light Illumination Enabled by Polarization Multiplexing Metasurface for Super-Resolution Imaging.

The phase-shifting structured light illumination technique is widely used in imaging but often relies on mechanical translation stages or spatial light modulators, leading to system instability, low displacement accuracy, and limited integration feasibility. In response to these challenges, we propose and demonstrate an approach for generating far-field phase-shifting structured light using a polarization multiplexing metasurface. By controlling the polarization states of incident and transmitted light, the metasurface creates a three-step displacement of structured light, eliminating the need to move samples or illumination sources. As a proof of concept, we experimentally demonstrate microscopic imaging using structured light illumination generated by metasurfaces, extracting high-frequency information from objects, and surpassing the diffraction limit. The proposed metasurface platform offers a promising approach for developing compact and robust phase-shifting imaging systems, with broad prospects in quantitative detection, machine vision, and beyond.

Ambient light spectrum affects larval Mexican jumping bean moth (Cydia saltitans) behavior despite light obstruction from host seed.

Spectral differences in ambient light can affect animal behavior and convey crucial information about an individual's environment. The ability to perceive and respond to differences in ambient light varies widely by taxa and is shaped by a species' ecology. Mexican jumping bean moths, Cydia saltitans, spend their entire larval period encased in fallen host seeds and contend with potentially lethal environmental temperatures when host seeds are in direct sunlight. We investigate if and how C. saltitans larvae in host seeds respond to lighting conditions associated with these thermal risks. In a temperature-controlled experiment, we identified that larvae demonstrated distinct behavioral ("jumping") responses corresponding to four lighting treatments (white, red, green, and purple), despite extremely minimal light penetration through host seed walls. Red light induced the greatest larval activity (measured by probability of movement and by displacement from origin), suggesting that larvae have mechanisms to perceive low levels of red light and/or to detect subtle increases in heat produced by red/near infrared-biased light spectra, possibly providing them with an early-warning mechanism against thermal stress. Our findings highlight the interplay of environmental lighting, behavior, and potential thermosensory adaptations in a species with a visually constrained environment.

Posterior approach to correct for focal plane offsets in lattice light-sheet structured illumination microscopy.

Significance: Lattice light-sheet structured illumination microscopy (latticeSIM) has proven highly effective in producing three-dimensional images with super resolution rapidly and with minimal photobleaching. However, due to the use of two separate objectives, sample-induced aberrations can result in an offset between the planes of excitation and detection, causing artifacts in the reconstructed images. Aim: We introduce a posterior approach to detect and correct the axial offset between the excitation and detection focal planes in latticeSIM and provide a method to minimize artifacts in the reconstructed images. Approach: We utilized the residual phase information within the overlap regions of the laterally shifted structured illumination microscopy information components in frequency space to retrieve the axial offset between the excitation and the detection focal planes in latticeSIM. Results: We validated our technique through simulations and experiments, encompassing a range of samples from fluorescent beads to subcellular structures of adherent cells. We also show that using transfer functions with the same axial offset as the one present during data acquisition results in reconstructed images with minimal artifacts and salvages otherwise unusable data. Conclusion: We envision that our method will be a valuable addition to restore image quality in latticeSIM datasets even for those acquired under non-ideal experimental conditions.

Low-light image enhancement using generative adversarial networks.

In low-light environments, the amount of light captured by the camera sensor is reduced, resulting in lower image brightness. This makes it difficult to recognize or completely lose details in the image, which affects subsequent processing of low-light images. Low-light image enhancement methods can increase image brightness while better-restoring color and detail information. A generative adversarial network is proposed for low-quality image enhancement to improve the quality of low-light images. This network consists of a generative network and an adversarial network. In the generative network, a multi-scale feature extraction module, which consists of dilated convolutions, regular convolutions, max pooling, and average pooling, is designed. This module can extract low-light image features from multiple scales, thereby obtaining richer feature information. Secondly, an illumination attention module is designed to reduce the interference of redundant features. This module assigns greater weight to important illumination features, enabling the network to extract illumination features more effectively. Finally, an encoder-decoder generative network is designed. It uses the multi-scale feature extraction module, illumination attention module, and other conventional modules to enhance low-light images and improve quality. Regarding the adversarial network, a dual-discriminator structure is designed. This network has a global adversarial network and a local adversarial network. They determine if the input image is actual or generated from global and local features, enhancing the performance of the generator network. Additionally, an improved loss function is proposed by introducing color loss and perceptual loss into the conventional loss function. It can better measure the color loss between the generated image and a normally illuminated image, thus reducing color distortion during the enhancement process. The proposed method, along with other methods, is tested using both synthesized and real low-light images. Experimental results show that, compared to other methods, the images enhanced by the proposed method are closer to normally illuminated images for synthetic low-light images. For real low-light images, the images enhanced by the proposed method retain more details, are more apparent, and exhibit higher performance metrics. Overall, compared to other methods, the proposed method demonstrates better image enhancement capabilities for both synthetic and real low-light images.

A Rapid Nanofocusing Method for a Deep-Sea Gene Sequencing Microscope Based on Critical Illumination.

In the deep-sea environment, the volume available for an in-situ gene sequencer is severely limited. In addition, optical imaging systems are subject to real-time, large-scale defocusing problems caused by ambient temperature fluctuations and vibrational perturbations. To address these challenges, we propose an edge detection algorithm for defocused images based on grayscale gradients and establish a defocus state detection model with nanometer resolution capabilities by relying on the inherent critical illumination light field. The model has been applied to a prototype deep-sea gene sequencing microscope with a 20× objective. It has demonstrated the ability to focus within a dynamic range of ±40 µm with an accuracy of 200 nm by a single iteration within 160 ms. By increasing the number of iterations and exposures, the focusing accuracy can be refined to 78 nm within a dynamic range of ±100 µm within 1.2 s. Notably, unlike conventional photoelectric hill-climbing, this method requires no additional hardware and meets the wide dynamic range, speed, and high-accuracy autofocusing requirements of deep-sea gene sequencing in a compact form factor.

Quantum illumination and quantum radar: a brief overview.

Quantum illumination (QI) and quantum radar have emerged as potentially groundbreaking technologies, leveraging the principles of quantum mechanics to revolutionise the field of remote sensing and target detection. The protocol, particularly in the context of quantum radar, has been subject to a great deal of aspirational conjecture as well as criticism with respect to its realistic potential. In this review, we present a broad overview of the field of quantum target detection focusing on QI and its potential as an underlying scheme for a quantum radar operating at microwave frequencies. We provide context for the field by considering its historical development and fundamental principles. Our aim is to provide a balanced discussion on the state of theoretical and experimental progress towards realising a working QI-based quantum radar, and draw conclusions about its current outlook and future directions.

Metamaterial-Assisted Illumination Nanoscopy with Exceptional Axial Resolution.

Recent advancements in optical metamaterials have opened new possibilities in the exciting field of super-resolution microscopies. The far-field metamaterial-assisted illumination nanoscopies (MAINs) have, very recently, enhanced the lateral resolution to one-fifteenth of the optical wavelength. However, the axial localization accuracy of fluorophores in the MAINs remains rarely explored. Here, a MAIN with a nanometer-scale axial localization accuracy is demonstrated by monitoring the distance-dependent photobleaching dynamics of the fluorophores on top of an organic hyperbolic metamaterial (OHM) substrate under a wide-field single-objective microscope. With such a regular experimental configuration, 3D imaging of various biological samples with the resolution of ≈40 nm in the lateral dimensions and ≈5 nm in the axial dimension is realized. The demonstrated imaging modality enables the resolution of the 3D morphology of nanoscopic cellular structures with a significantly simplified experimental setup.

Identification of z-axis filopodia in growth cones using super-resolution microscopy.

A growth cone is a highly motile tip of an extending axon that is crucial for neural network formation. Three-dimensional-structured illumination microscopy, a type of super-resolution light microscopy with a resolution that overcomes the optical diffraction limitation (ca. 200 nm) of conventional light microscopy, is well suited for studying the molecular dynamics of intracellular events. Using this technique, we discovered a novel type of filopodia distributed along the z-axis ("z-filopodia") within the growth cone. Z-filopodia were typically oriented in the direction of axon growth, not attached to the substratum, protruded spontaneously without microtubule invasion, and had a lifetime that was considerably shorter than that of conventional filopodia. Z-filopodia formation and dynamics were regulated by actin-regulatory proteins, such as vasodilator-stimulated phosphoprotein, fascin, and cofilin. Chromophore-assisted laser inactivation of cofilin induced the rapid turnover of z-filopodia. An axon guidance receptor, neuropilin-1, was concentrated in z-filopodia and was transported together with them, whereas its ligand, semaphorin-3A, was selectively bound to them. Membrane domains associated with z-filopodia were also specialized and resembled those of lipid rafts, and their behaviors were closely related to those of neuropilin-1. The results suggest that z-filopodia have unique turnover properties, and unlike xy-filopodia, do not function as force-generating structures for axon extension.

Effect of Photoperiod and Illuminance on Daily Activity Patterns, Physiology, and NPY Peptide Expression in Migratory Redheaded Buntings (Emberiza bruniceps).

INTRODUCTION: Light is the primary source of energy and regulates seasonal changes in physiology and behavior. The role of photoperiod has been much investigated in several bird species, but the role of illumination in seasonal adaptations of passerine finches is less understood. We, therefore, investigated the effects of photoperiod and illuminance on migratory physiology in a Palearctic-Indian migratory finch, redheaded bunting (Emberiza bruncieps). METHODS: Photosensitive buntings maintained under short days (8L:16D) were divided into three groups receiving 5, 25, and 100 lux of white daytime illuminance, respectively. Thereafter, using photoperiodic manipulation three life history states, i.e., nonmigratory (NM), premigratory (PM), and migratory (MIG) states were induced in the buntings. The birds in the MIG state were consecutively perfused after seven nights of Zugunruhe (nighttime migratory restlessness) for neuropeptide Y (NPY)-immunohistochemistry, which is involved in a wide range of functions including energy homeostasis, vision, and fat deposition in birds. RESULTS: We found differential effects of illuminance on locomotor activity and physiology. Photostimulated birds showed intense nighttime activity in the MIG state. We observed premigratory hyperphagia in the birds, with increased food intake in the 100 lux group, which was reflected in the body mass gain in the MIG state. NPY expression on the periphery of the nucleus rotundus suggests its potential role in visual acuity, where the NPY-cell count significantly decreased under 25 lux illumination. CONCLUSION: We demonstrate that migrating birds may also experience physiological effects from changes in daytime illumination. We observed illuminance-dependent variations in the quantity of food consumed by the birds. It indicates that the illuminance may also impact the encephalic centers that control food intake.

Oxygen Switchable Photo-Hydrovoltaic Effect along the Silicon-Water Interface.

Moving boundaries of electrical double layers have shown promising capability in driving directional electron flows in solids, leading to a range of hydrovoltaic effects. The recent discovery of a photohydrovoltaic phenomenon utilizes a moving illumination zone to generate moving boundaries with different properties at the solid-water interface, referred to as the kinetic photovoltaic effect. Here, oxygen was found to act as a chemical switch to turn on and off the kinetic photovoltaic effect. Introducing oxygen would rapidly diminish the kinetic photovoltage in p-Si. On the contrary, degassing oxygen leads to a gradual recovery, whose rate can be facilely speeded up by more than one order through electrostatic gating. Mechanistic investigations of the oxygen switch behavior uncovered a dependence of surface band bending intensity of silicon on oxygen adsorption, which highlights the role of gas molecules, often overlooked, in applications based on semiconductor-liquid interfaces, such as photoelectrochemistry.