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In neuroinflammation, astrocytes play multifaceted roles that regulate the neuronal environment. Astrocytes sense and respond to pro-inflammatory cytokines (CKs) and, by a repertoire of intracellular Ca2+ signaling, contribute to disease progression. Therapeutic approaches wish to reduce the overactivation in Ca2+ signaling in inflammatory-reactive astrocytes to restore dysregulated cellular changes. Cell-targeting therapeutics might take advantage by the use of nanomaterial-multifunctional platforms such as graphene oxide (GO). GO biomedical applications in the nervous system involve therapeutic delivery and sensing, and GO flakes were shown to enable interfacing of neuronal and glial membrane dynamics. We exploit organotypic spinal cord cultures and optical imaging to explore Ca2+ changes in astrocytes, and we report, when spinal tissue is exposed to CKs, neuroinflammatory-associated modulation of resident glia. We show the efficacy of GO to revert these dynamic changes in astrocytic reactivity to CKs, and we translate this potential in an animal model of immune-mediated neuroinflammatory disease.

Tracers for bimodal optical imaging and positron emission tomography unite multiple ad-vantages in a single molecule. Their tumor-specific uptake can be visualized after their PET-activation by radiofluorination via PET/CT or PET/MRI allowing for staging or therapy planning, while their non-radioactive moiety additionally facilitates the visualization of malig-nant tissue during intraoperative fluorescence-guided surgery or in histological assessments. The silicon-bridged xanthene core offers the opportunity for radiofluorination with SiFA isotope exchange to obtain a small molecule PET-activatable NIR dye that can be linked to different tar-get vectors. Herein, we demonstrate the PET-activation of a fluorinated silicon pyronine, be-longing to a class of low-molecular weight fluorescence dyes with a large Stokes shift and sol-vent-dependent NIR dye properties, with successful radiochemical conversion. Moreover, a li-brary of unusually functionalized, red-shifted silicon rhodamines was synthesized, which can be easily conjugated by amide bond formation or ‘click-reaction’ approaches.

Intestinal organoids represent a three-dimensional cell culture system mimicking the mammalian intestine. The application of single-cell ablation for defined wounding via a femtosecond laser system within the crypt base allowed us to study cell dynamics during epithelial restitution. Neighboring cells formed a contractile actin ring encircling the damaged cell, changed the cellular aspect ratio, and immediately closed the barrier. Using traction force microscopy, we observed major forces at the ablation site and additional forces on the crypt sides. Inhibitors of the actomyosin-based mobility of the cells led to the failure of restoring the barrier. Close to the ablation site, high-frequency calcium flickering and propagation of calcium waves occured that synchronized with the contraction of the epithelial layer. We observed an increased signal and nuclear translocation of YAP-1. In conclusion, our approach enabled, for the first time, to unveil the intricacies of epithelial restitution beyond in vivo models by employing precise laser-induced damage in colonoids.

Two-photon absorption (TPA) fluorescence imaging holds great promise in diagnostics and biomedicine owing to its unparalleled spatiotemporal resolution. However, the adaptability and applicability of currently available TPA probes, which act as a critical element for determining the imaging contrast effect, is severely challenged by limited photo-luminescence in vivo. This is particularly a result of uncontrollable aggregation that causes fluorescence quenching, and inevitable photo-oxidation in harsh physiological milieu, which normally leads to bleaching of the dye. Herein, we describe the remarkably enhanced TPA fluorescence imaging capacity of self-assembling near-infrared (NIR) cyanine dye-based nanoprobes (NPs), which can be explained by a photo-oxidation enhanced emission mechanism. Singlet oxygen generated during photo-oxidation enables chromophore dimerization to form TPA intermediates responsible for enhanced TPA fluorescence emission. The resulting NPs possess uniform size distribution, excellent stability, more favorable TPA cross-section and anti-bleaching ability than a popular TPA probe rhodamine B (RhB). These properties of cyanine dye-based TPA NPs promote their applications in visualizing blood circulation and tumoral accumulation in real-time, even to cellular imaging in vivo. The photo-oxidation enhanced emission mechanism observed in these near-infrared cyanine dye-based nanoaggregates opens an avenue for design and development of more advanced TPA fluorescence probes.

In assessing food microbial safety, the presence of Escherichia coli is a critical indicator of fecal contamination. However, conventional detection methods require the isolation of bacterial macrocolonies for biochemical or genetic characterization, which takes a few days and is labor-intensive. In this study, we show that the real-time object detection and classification algorithm You Only Look Once version 4 (YOLOv4) can accurately identify the presence of E. coli at the microcolony stage after a 3-h cultivation. Integrating with phase-contrast microscopic imaging, YOLOv4 discriminated E. coli from seven other common foodborne bacterial species with an average precision of 94%. This approach also enabled the rapid quantification of E. coli concentrations over 3 orders of magnitude with an R2 of 0.995. For romaine lettuce spiked with E. coli (10 to 103 CFU/g), the trained YOLOv4 detector had a false-negative rate of less than 10%. This approach accelerates analysis and avoids manual result determination, which has the potential to be applied as a rapid and user-friendly bacterial sensing approach in food industries. IMPORTANCE A simple, cost-effective, and rapid method is desired to identify potential pathogen contamination in food products and thus prevent foodborne illnesses and outbreaks. This study combined artificial intelligence (AI) and optical imaging to detect bacteria at the microcolony stage within 3 h of inoculation. This approach eliminates the need for time-consuming culture-based colony isolation and resource-intensive molecular approaches for bacterial identification. The approach developed in this study is broadly applicable for the identification of diverse bacterial species. In addition, this approach can be implemented in resource-limited areas, as it does not require expensive instruments and significantly trained human resources. This AI-assisted detection not only achieves high accuracy in bacterial classification but also provides the poten

The study was aimed to evaluate the performance of a newly developed spectroscopy-based non-invasive and noncontact device (SAMIRA) for the simultaneous measurement of hemoglobin, bilirubin and oxygen saturation as an alternative to the invasive biochemical method of blood sampling. The accuracy of the device was assessed in 4318 neonates having incidences of either anemia, jaundice, or hypoxia. Transcutaneous bilirubin, hemoglobin and blood saturation values were obtained by the newly developed instrument which was corroborated with the biochemical blood tests by expert clinicians. The instrument is trained using Artificial Neural Network Analysis to increase the acceptability of the data. The artificial intelligence incorporated within the instrument determines the disease condition of the neonate. The Pearson's correlation coefficient, r was found to be 0.987 for hemoglobin estimation and 0.988 for bilirubin and blood gas saturation respectively. The bias and the limits of agreement for the measurement of all the three parameters were within the clinically acceptance limit.

The retina, behind the transparent optics of the eye, is the only neural tissue whose physiology and pathology can be non-invasively probed by optical microscopy. The aberrations intrinsic to the mouse eye, however, prevent high-resolution investigation of retinal structure and function in vivo. Optimizing the design of a two-photon fluorescence microscope (2PFM) and sample preparation procedure, we found that adaptive optics (AO), by measuring and correcting ocular aberrations, is essential for resolving putative synaptic structures and achieving three-dimensional cellular resolution in the mouse retina in vivo. Applying AO-2PFM to longitudinal retinal imaging in transgenic models of retinal pathology, we characterized microvascular lesions with sub-capillary details in a proliferative vascular retinopathy model, and found Lidocaine to effectively suppress retinal ganglion cell hyperactivity in a retinal degeneration model. Tracking structural and functional changes at high-resolution longitudinally, AO-2PFM enables microscopic investigations of retinal pathology and pharmacology for disease diagnosis and treatment in vivo.

SignificancePerturbation and differential Monte Carlo (pMC/dMC) methods, used in conjunction with nonlinear optimization methods, have been successfully applied to solve inverse problems in diffuse optics. Application of pMC to systems over a large range of optical properties requires optimal "placement" of baseline conventional Monte Carlo (cMC) simulations to minimize the pMC variance. The inability to predict the growth in pMC solution uncertainty with perturbation size limits the application of pMC, especially for multispectral datasets where the variation of optical properties can be substantial.AimWe aim to predict the variation of pMC variance with perturbation size without explicit computation of perturbed photon weights. Our proposed method can be used to determine the range of optical properties over which pMC predictions provide sufficient accuracy. This method can be used to specify the optical properties for the reference cMC simulations that pMC utilizes to provide accurate predictions over a desired optical property range.ApproachWe utilize a conventional error propagation methodology to calculate changes in pMC relative error for Monte Carlo simulations. We demonstrate this methodology for spatially resolved diffuse reflectance measurements with ±20% scattering perturbations. We examine the performance of our method for reference simulations spanning a broad range of optical properties relevant for diffuse optical imaging of biological tissues. Our predictions are computed using the variance, covariance, and skewness of the photon weight, path length, and collision distributions generated by the reference simulation.ResultsWe find that our methodology performs best when used in conjunction with reference cMC simulations that utilize Russian Roulette (RR) method. Specifically, we demonstrate that for a proximal detector placed immediately adjacent to the source, we can estimate the pMC relative error within 5% of the true value for scattering perturba

The study of cellular behavior necessitates precise and efficient tools for measuring various cellular properties. This thesis focuses on the development and implementation of a novel platform for simultaneous dielectric and optical properties characterization of single cells and particles. The motivation behind this work stems from the need for accurate and efficient tools to study cellular behavior and recent advancements in sensors and electronics. Dielectric properties of cells and particles have significant value in various applications, including cell identification and sorting, disease diagnosis and monitoring, drug discovery and development, as well as provide valuable insights into their structural and functional characteristics. The objectives of this thesis project were to enhance the optical transit differential dielectrophoresis cytometer strategy, enable rapid deployment of automated experiments, and provide a reliable platform for future data collection. These objectives were achieved through the development of two generations of hardware platforms. The first generation hardware platform laid the foundation for further improvements and presented the potential of the method. Building upon this, the second generation hardware platform addressed the limitations identified in the first generation system, resulting in a more reliable and flexible system with enhanced specifications and performance. This platform enables faster and more efficient data collection, provides higher resolution particle size information, allows for multiwavelength captures, and enables simultaneous measurement of multiple DEP frequencies. The experimental and simulated results presented in this thesis demonstrate the system's capabilities, performance, and potential for further development. The platforms demonstrated the ability to differentiate particles based on particle size, shown promise in estimating cell viability and differentiating between different types of cancer cell

In this paper, an on-chip optical circulator on the InP-membrane-on-Si (IMOS) platform is demonstrated. The circulator is composed of two multi-mode interferometers (MMIs), four polarization converters (PCs), and a Cerium-doped Yttrium Iron Garnet (Ce:YIG) die. The Ce:YIG die is adhesively bonded on the InP membrane via a 125-nm thin bonding layer. Nonreciprocal phase shift (NRPS) is employed in the presence of a transverse magnetic field. The device works as a 4-port optical circulator with a maximum optical isolation of 27.0 dB and a minimum optical isolation of 18.6 dB for the TE mode. The measured optical isolation bandwidth is 0.12 nm. Two methods are proposed in order to improve the optical isolation bandwidth.

The use of a planar detection geometry in photoacoustic tomography results in the so-called limited-view problem due to the finite extent of the acoustic detection aperture. When images are reconstructed using one-step reconstruction algorithms, image quality is compromised by the presence of streaking artefacts, reduced contrast, image distortion and reduced signal-to-noise ratio. To mitigate this, model-based iterative reconstruction approaches based on least squares minimisation with and without total variation regularisation were evaluated using in-silico, experimental phantom, ex vivo and in vivo data. Compared to one-step reconstruction methods, it has been shown that iterative methods provide better image quality in terms of enhanced signal-to-artefact ratio, signal-to-noise ratio, amplitude accuracy and spatial fidelity. For the total variation approaches, the impact of the regularisation parameter on image feature scale and amplitude distribution was evaluated. In addition, the extent to which the use of Bregman iterations can compensate for the systematic amplitude bias introduced by total variation was studied. This investigation is expected to inform the practical application of model-based iterative image reconstruction approaches for improving photoacoustic image quality when using finite aperture planar detection geometries.

There are two theories of how the neocortex processes sound. The feature detection theory posits that cortical neurons process sound to extract its features, whereas, in the decoding theory, cortical neurons are viewed as the decoders of “sound objects”: specific spectrotemporal patterns. In this thesis, I assess both theories in a set of longitudinal experiments with simple and complex sounds presented to awake and isoflurane-anesthetized mice. For the feature detection theory, the auditory cortex (AC) maps of tone frequency and frequency modulation (FM) rate are updated. In these maps, new areas and topographic gradients are discovered. The maps are stable across a recording period and reproducible between brain states. Moreover, a region in the primary AC encodes for the FM direction, crucial in the perception of complex sounds. Finally, fast FM rate topographic areas are the most involved in processing MVocs. For the prediction theory, both AC and the dorsal neocortex (DC) were considered. First, network analysis revealed that AC and DC work together to process sounds, with the interaction level determined by acoustic complexity and brain state. Second, a classification analysis showed that both cortices are informative of the stimuli, with AC twice as informative as DC. Isoflurane-anesthesia maintains the informative power of AC but vanishes the power of DC, except for simple sounds. Lastly, a regression analysis between sounds and behavior (predictors) and the cortical activity (response) was performed to assess the power of stimuli to explain the cortical activity. The sound stimuli could explain AC and DC activity up to 68% and 32%, respectively. The higher explanation power of sound is observed for more complex sounds in the awake state. To conclude, despite the simplicity of the feature detection theory, the detection theory provides a superior understanding of the neocortical functions in auditory processing.

There are two theories of how the neocortex processes sound. The feature detection theory posits that cortical neurons process sound to extract its features, whereas, in the decoding theory, cortical neurons are viewed as the decoders of “sound objects”: specific spectrotemporal patterns. In this thesis, I assess both theories in a set of longitudinal experiments with simple and complex sounds presented to awake and isoflurane-anesthetized mice. For the feature detection theory, the auditory cortex (AC) maps of tone frequency and frequency modulation (FM) rate are updated. In these maps, new areas and topographic gradients are discovered. The maps are stable across a recording period and reproducible between brain states. Moreover, a region in the primary AC encodes for the FM direction, crucial in the perception of complex sounds. Finally, fast FM rate topographic areas are the most involved in processing MVocs. For the prediction theory, both AC and the dorsal neocortex (DC) were considered. First, network analysis revealed that AC and DC work together to process sounds, with the interaction level determined by acoustic complexity and brain state. Second, a classification analysis showed that both cortices are informative of the stimuli, with AC twice as informative as DC. Isoflurane-anesthesia maintains the informative power of AC but vanishes the power of DC, except for simple sounds. Lastly, a regression analysis between sounds and behavior (predictors) and the cortical activity (response) was performed to assess the power of stimuli to explain the cortical activity. The sound stimuli could explain AC and DC activity up to 68% and 32%, respectively. The higher explanation power of sound is observed for more complex sounds in the awake state. To conclude, despite the simplicity of the feature detection theory, the detection theory provides a superior understanding of the neocortical functions in auditory processing.

This paper presents a material classification method using an off-the-shelf Time-of-Flight (ToF) camera. The proposed method is built upon a key observation that the depth measurement by a ToF camera is distorted for objects with certain materials, especially with translucent materials. We show that this distortion is due to the variation of time domain impulse responses across materials and also due to the measurement mechanism of the ToF cameras. Specifically, we reveal that the amount of distortion varies according to the modulation frequency of the ToF camera, the object material, and the distance between the camera and object. Our method uses the depth distortion of ToF measurements as a feature for classification and achieves material classification of a scene. Effectiveness of the proposed method is demonstrated by numerical evaluations and real-world experiments, showing its capability of material classification, even for visually indistinguishable objects.

We present a novel method for estimating 3-D positions of near light sources by using highlights on the outside and inside of a single clear hollow sphere. Conventionally, the positions of near light sources have been estimated by using observed highlights on multiple reference objects, e.g. mirror balls., ICPR 2012 : 21th International Conference on Pattern Recognition , Nov 11-15, 2012 , Tsukuba, Japan

This paper proposes a novel image inpainting method to remove undesired objects in an image. Conventionally, missing regions are filled in using similar textures in an image as exemplars. However, unnatural textures are often generated due to the paucity of available samples. In this study, we take into account symmetric transformation of texture patterns to increase exemplars. To generate plausible textures in missing regions with variously transformed patterns, we employ two approaches: (1) we use spatial coherence of texture patterns when searching for similar patterns, and (2) we define a new degree of confidence of exemplars for determining pixel values. The effectiveness of the proposed method is demonstrated by comparing results by three methods., ICPR 2012 : 21th International Conference on Pattern Recognition , Nov 11-15, 2012 , Tsukuba, Japan

This paper describes a method for recovering appearance of inner slices of translucent objects. The outer appearance of translucent objects is a summation of the appearance of slices at all depths, where each slice is blurred by depth-dependent point spread functions (PSFs). By exploiting the difference of low-pass characteristics of depth-dependent PSFs, we develop a multi-frequency illumination method for obtaining the appearance of individual inner slices using a coaxial projector-camera setup. Specifically, by measuring the target object with varying the spatial frequency of checker patterns emitted from a projector, our method recovers inner slices via a simple linear solution method. We quantitatively evaluate accuracy of the proposed method by simulations and show qualitative recovery results using real-world scenes., 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),7-12 June 2015,Boston, MA, USA

In this paper, we proposed an improved optical scattering tomography for optically dense media. We model a material by many layers with voxels, and light scattering by a distribution from a voxel in one layer to other voxels in the next layer. Then we write attenuation of light along a light path by an inner product of vectors, and formulate the scattering tomography as an inequality constraint optimization problem solved by an interior point method. To improve the accuracy, we solve simultaneously four configurations of a multiple-scattering tomography, however, this would increase the computational cost by a factor of four if we simply solved the problem four times. To reduce the computation cost, we introduce a quasi-Newton method to update the inverse of a Hessian matrix used in the iteration of the interior point method. We show experimental results with numerical simulation for evaluating the proposed method and comparisons with our previous work., 2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)28-30 Jan. 2015,Mokpo, South Korea

In lithography process, Sub-Resolution Assist Features (SRAFs), as an essential resolution enhancement technique (RET), is applied to improve the pattern fidelity and enlarge the process window. In this paper, we propose a robust constraint-aware SRAF generation method based on continuous transmission mask (CTM). The intensity distribution on the continuous transmission mask is extracted to guide the SRAF generation. The SRAF insertion also honors the design rules, which is formulated as integer programming with quadratic constraints and solved by a fast yet efficient algorithm. A fast probe-based SRAF evolution method is proposed to determine the shapes of SRAFs. The effectiveness and efficiency are demonstrated based on the experimental results. IEEE

As the feature size of advanced integrated circuits keeps shrinking, resolution enhancement techniques (RETs) are utilized to improve the printability in the lithography process. Optical proximity correction (OPC) is one of the most widely used RETs aiming at compensating the mask to generate a more precise wafer image. In this article, we put forward a level set-based OPC approach with high mask optimization quality and fast convergence. In order to suppress the disturbance of the condition fluctuation in the lithography process, we propose a new process window-aware cost function. Then, a novel momentum-based evolution technique is adopted, which demonstrates substantial improvement. We also propose a self-adaptive conjugate gradient method that promises a higher optimization stability and less consuming time. Moreover, the graphics processing unit (GPU) is leveraged for accelerating the proposed algorithm. We take the output masks from a machine learning-based mask optimization flow as the input and work as the post process to refine the quasi-optimized masks. Experimental results on ICCAD 2013 benchmarks show that our algorithm outperforms all previous OPC algorithms in both solution quality and runtime overhead.

Applying an image processing algorithm independently to each video frame often leads to temporal inconsistency in the resulting video. To address this issue, we present a novel and general approach for blind video temporal consistency. Our method is only trained on a pair of original and processed videos directly instead of a large dataset. Unlike most previous methods that enforce temporal consistency with optical flow, we show that temporal consistency can be achieved by training a convolutional network on a video with Deep Video Prior (DVP). Moreover, a carefully designed iteratively reweighted training strategy is proposed to address the challenging multimodal inconsistency problem. We demonstrate the effectiveness of our approach on 7 computer vision tasks on videos. Extensive quantitative and perceptual experiments show that our approach obtains superior performance than state-of-the-art methods on blind video temporal consistency. We further extend DVP to video propagation and demonstrate its effectiveness in propagating three different types of information (color, artistic style, and object segmentation). A progressive propagation strategy with pseudo labels is also proposed to enhance DVP's performance on video propagation. Our source codes are publicly available at https://github.com/ChenyangLEI/deep-video-prior. IEEE

Genitourinary syndrome of menopause (GSM) negatively affects more than half of postmenopausal women. Energy-based therapy has been explored as a minimally invasive treatment for GSM; however, its mechanism of action and efficacy is controversial. Here, we report on a pilot imaging study conducted on a small group of menopause patients undergoing laser treatment. Intravaginal optical coherence tomography (OCT) endoscope was used to quantitatively monitor the changes in the vaginal epithelial thickness (VET) during fractional-pixel CO2 laser treatment. Eleven patients with natural menopause and one surgically induced menopause patient were recruited in this clinical study. Following the laser treatment, 6 out of 11 natural menopause patient showed increase in both proximal and distal VET, while two natural menopause patient showed increase in VET in only one side of vaginal tract. Furthermore, the patient group that showed increased VET had thinner baseline VET compared to the patients that showed decrease in VET after laser treatment. These results demonstrate the potential utility of intravaginal OCT endoscope in evaluating the vaginal tissue integrity and tailoring vaginal laser treatment on a per-person basis, with the potential to monitor other treatment procedures.

Synaptic transmission via neurochemical release is the fundamental process that integrates and relays encoded information in the brain to regulate physiological function, cognition, and emotion. To unravel the biochemical, biophysical, and computational mechanisms of signal processing, one needs to precisely measure the neurochemical release dynamics with molecular and cell-type specificity and high resolution. Here we reviewed the development of analytical, electrochemical, and fluorescence imaging approaches to detect neurotransmitter and neuromodulator release. We discussed the advantages and practicality in implementation of each technology for ease-of-use, flexibility for multimodal studies, and challenges for future optimization. We hope this review will provide a versatile guide for tool engineering and applications for recording neurochemical release.

Fluorescence imaging is an indispensable method for studying biological processes non-invasively in cells and transparent organisms. Extension into the shortwave infrared (SWIR, 1000-2000 nm) region of the electromagnetic spectrum has allowed for imaging in mammals with unprecedented depth and resolution for optical imaging. In this review, we summarize recent advances in imaging technologies, dye scaffold modifications, and incorporation of these dyes into probes for SWIR imaging in mice. Finally, we offer an outlook on the future of SWIR detection in the field of chemical biology.

Recent advances in fluorescence imaging permit large-scale recording of neural activity and dynamics of neurochemical release with unprecedented resolution in behaving animals. Calcium imaging with highly optimized genetically encoded indicators provides a mesoscopic view of neural activity from genetically defined populations at cellular and subcellular resolutions. Rigorously improved voltage sensors and microscopy allow for robust spike imaging of populational neurons in various brain regions. In addition, recent protein engineering efforts in the past few years have led to the development of sensors for neurotransmitters and neuromodulators. Here, we discuss the development and applications of these genetically encoded fluorescent indicators in reporting neural activity in response to various behaviors in different biological systems as well as in drug discovery. We also report a simple model to guide sensor selection and optimization.

Optical imaging of calcium signals in the brain has enabled researchers to observe the activity of hundreds-to-thousands of individual neurons simultaneously. Current methods predominantly use morphological information, typically focusing on expected shapes of cell bodies, to better identify neurons in the field-of-view. The explicit shape constraints limit the applicability of automated cell identification to other important imaging scales with more complex morphologies, e.g., dendritic or widefield imaging. Specifically, fluorescing components may be broken up, incompletely found, or merged in ways that do not accurately describe the underlying neural activity. Here we present Graph Filtered Temporal Dictionary (GraFT), a new approach that frames the problem of isolating independent fluorescing components as a dictionary learning problem. Specifically, we focus on the time-traces-the main quantity used in scientific discovery-and learn a time trace dictionary with the spatial maps acting as the presence coefficients encoding which pixels the time-traces are active in. Furthermore, we present a novel graph filtering model which redefines connectivity between pixels in terms of their shared temporal activity, rather than spatial proximity. This model greatly eases the ability of our method to handle data with complex non-local spatial structure. We demonstrate important properties of our method, such as robustness to morphology, simultaneously detecting different neuronal types, and implicitly inferring number of neurons, on both synthetic data and real data examples. Specifically, we demonstrate applications of our method to calcium imaging both at the dendritic, somatic, and widefield scales.

The innate immune system is critical for infection survival. Drosophila melanogaster is a key model for understanding the evolution and dynamics of innate immunity. Current toolsets for fly infection studies are limited in throughput and, because of their destructive nature, cannot generate longitudinal measurements in individual animals. We report a bioluminescent imaging strategy enabling non-invasive characterization of pathogen load. By using Escherichia coli expressing the ilux operon, we demonstrate that photon flux from autobioluminescent bacteria can be used to monitor pathogen loads in individual, living flies. Because animal sacrifice is not necessary to estimate pathogen load, stochastic responses to infection can be characterized in individuals over time. The high temporal resolution of bioluminescence imaging enables visualization of the dynamics of microbial clearance on the hours time-scale. This non-invasive imaging strategy provides a simple and scalable platform to observe changes in pathogen load in vivo over time.

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.

End-to-end trained convolutional neural networks have led to a breakthrough in optical flow estimation. The most recent advances focus on improving the optical flow estimation by improving the architecture and setting a new benchmark on the publicly available MPI-Sintel dataset. Instead, in this article, we investigate how deep neural networks estimate optical flow. A better understanding of how these networks function is important for (i) assessing their generalization capabilities to unseen inputs, and (ii) suggesting changes to improve their performance. For our investigation, we focus on FlowNetS, as it is the prototype of an encoder-decoder neural network for optical flow estimation. Furthermore, we use a filter identification method that has played a major role in uncovering the motion filters present in animal brains in neuropsychological research. The method shows that the filters in the deepest layer of FlowNetS are sensitive to a variety of motion patterns. Not only do we find translation filters, as demonstrated in animal brains, but thanks to the easier measurements in artificial neural networks, we even unveil dilation, rotation, and occlusion filters. Furthermore, we find similarities in the refinement part of the network and the perceptual filling-in process which occurs in the mammal primary visual cortex., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Education AE, Control & Simulation

Surgical management of head and neck cancer requires a careful balance between complete resection of malignancy and preservation of function. Surgeons must also determine whether to resect important cranial nerves that harbor perineural invasion (PNI), as sacrificing nerves can result in significant morbidity including facial paralysis. Our group has previously reported that Dynamic Optical Contrast Imaging (DOCI), a novel non-invasive imaging system, can determine margins between malignant and healthy tissues. Herein, we use an in vivo murine model to demonstrate that DOCI can accurately identify cancer margins and perineural invasion, concordant with companion histology. Eight C3H/HeJ male mice were injected subcutaneously into the bilateral flanks with SCCVIISF, a murine head and neck cancer cell line. DOCI imaging was performed prior to resection to determine margins. Both tumor and margins were sent for histologic sectioning. After validating that DOCI can delineate HNSCC margins, we investigated whether DOCI can identify PNI. In six C3H/HeJ male mice, the left sciatic nerve was injected with PBS and the right with SCCVIISF. After DOCI imaging, the sciatic nerves were harvested for histologic analysis. All DOCI images were acquired intraoperatively and in real-time (10 s per channel), with an operatively relevant wide field of view. DOCI values distinguishing cancer from adjacent healthy tissue types were statistically significant (P < 0.05). DOCI imaging was also able to detect perineural invasion with 100% accuracy compared to control (P < 0.05). DOCI allows for intraoperative, real-time visualization of malignant and healthy tissue margins and perineural invasion to help guide tumor resection.

The development of new diagnostic tools in tumor pathology allows the optimization of individualized therapies in cancer patients. The functional optical image provides a unique opportunity to identify the pathophysiological characteristics of each tumor in a non-invasive way. Although fluorescent recombinant affibodies and nanobodies, capable of detecting certain membrane proteins present in tumor cells, has been described, the use of bioluminescent molecules is gaining a great impact in this field due to its high sensitivity. In this work, we characterize a new luciferase from the Metridia lucens copepod (MlLuc) and develop a novel bioluminescent recombinant affibody (MlLuc-aff) capable of recognizing the HER2 receptors that are overexpressed in breast cancer tumors. For this purpose, the thermostability and pH sensitivity of MlLuc1.1 were determined, showing no significant changes in the activity among temperatures between 4 and 70 °C, and with a maximum of brightness at pH 8.0. Furthermore, MlLuc-aff was able to accurately detect HER2 receptors expressed in the SK-BR-3 cells. Future applications of this new tracer can contribute to the early diagnosis of breast cancer patients and the assessment of the efficacy of the treatment

Considering the unavailability of labeled data sets in remote sensing change detection, this letter presents a novel and low complexity unsupervised change detection method based on the combination of similarity and dissimilarity measures: mutual information (MI), disjoint information (DI), and local dissimilarity map (LDM). MI and DI are calculated on sliding windows with a step of 1 pixel for each pair of channels of both images. The resulting scalar values, weighted by q and m coefficients, are multiplied by the values of the center pixels of the windows weighted by p to remove the textures on images. The changes are detected using, respectively, the grayscale LDM and color LDM. A sliding window is then used on the color LDM and each pixel is characterized by a two-parameter Weibull distribution. Binarized change maps can be obtained by using a k-means clustering on the model parameters. Experiments on optical aerial image data set show that the proposed method produces comparable, even better results, to the state-of-the-art methods in terms of recall, precision, and F-measure.

We present an optics-free CMOS image sensor that incorporates a novel time-gated dual-photodiode pixel design to allow filter- and lens-less image acquisition of near-infrared-excited (NIR-excited) upconverting nanoparticles. Recent biomedical advances have highlighted the benefits of NIR excitation, but NIR interaction with silicon has remained a challenge, even with high-performance optical blocking filters. Using a secondary diode and a dual-photodiode design, this sensor is able to remove the 100s of mV of NIR background on pixels and bring it down to single-digit mV level, nearing its noise floor of 2.2 mV rms, not achievable with any optical filter. Non-linear effects of background cancellation using the diode pair has been mitigated using an initial one-time pixel-level curve fitting and calibration in a post-processing setting. This imager comprises a highly linear 11 fF metal-oxide-metal (MOM) capacitor and includes integrated angle-selective gratings to reject oblique light and enhance sharpness. Each pixel also includes two distinct correlated double sampling schemes, to remove low frequency flicker noise and systematic offset in the datapath. We demonstrate the performance of this imager using pulsed NIR-excited upconverting nanoparticles on standard United-States-Air-Force (USAF) resolution targets and achieve an SNR of 15 dB, while keeping NIR background below 6 mV. This 36-by-80-pixel array measures only 2.3 mm by 4.8 mm and can be thinned down to 25 µm, allowing it to become surgically compatible with intraoperative instruments and equipment, while remaining optics-free.

Optical micromachines have the potential to improve the capabilities of optical tweezers by amplifying forces and allowing for indirect handling and probing of specimens. However, systematic design and testing of micromachine performance is still an emerging field. In this work we have designed and tested an unsupported microlever, suitable for general-purpose optical tweezer studies, that demonstrates stable trapping performance and repeatable doubling of applied forces. Stable trapping was ensured by analysing images to monitor focus shift when levers oscillated repeatedly, before the best-performing design was selected for force amplification. This study also shows that direct measurement of trap stiffness using the equipartition theorem appears to be a valid method for measuring applied forces on the spherical handles of microlevers., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Carlas Smith, Micro and Nano Engineering

Oral cancer is a cancer type that is widely prevalent in low-and middle-income countries with a high mortality rate, and poor quality of life for patients after treatment. Early treatment of cancer increases patient survival, improves quality of life and results in less morbidity and a better prognosis. To reach this goal, early detection of malignancies using technologies that can be used in remote and low resource areas is desirable. Such technologies should be affordable, accurate, and easy to use and interpret. This review surveys different technologies that have the potentials of implementation in primary health and general dental practice, considering global perspectives and with a focus on the population in India, where oral cancer is highly prevalent. The technologies reviewed include both sample-based methods, such as saliva and blood analysis and brush biopsy, and more direct screening of the oral cavity including fluorescence, Raman techniques, and optical coherence tomography. Digitalisation, followed by automated artificial intelligence based analysis, are key elements in facilitating wide access to these technologies, to non-specialist personnel and in rural areas, increasing quality and objectivity of the analysis while simultaneously reducing the labour and need for highly trained specialists., Funding: Linkoping University; Folktandvarden Stockholms la AB [7071]; Folktandvarden Region Dalarna, forskningsstiftelsen Folktandvarden Dalarna; VinnovaVinnova [2017-02447]; DBT-VINNOVA [2020-03611]

Oral cancer is a cancer type that is widely prevalent in low-and middle-income countries with a high mortality rate, and poor quality of life for patients after treatment. Early treatment of cancer increases patient survival, improves quality of life and results in less morbidity and a better prognosis. To reach this goal, early detection of malignancies using technologies that can be used in remote and low resource areas is desirable. Such technologies should be affordable, accurate, and easy to use and interpret. This review surveys different technologies that have the potentials of implementation in primary health and general dental practice, considering global perspectives and with a focus on the population in India, where oral cancer is highly prevalent. The technologies reviewed include both sample-based methods, such as saliva and blood analysis and brush biopsy, and more direct screening of the oral cavity including fluorescence, Raman techniques, and optical coherence tomography. Digitalisation, followed by automated artificial intelligence based analysis, are key elements in facilitating wide access to these technologies, to non-specialist personnel and in rural areas, increasing quality and objectivity of the analysis while simultaneously reducing the labour and need for highly trained specialists., Funding: Linkoping University; Folktandvarden Stockholms la AB [7071]; Folktandvarden Region Dalarna, forskningsstiftelsen Folktandvarden Dalarna; VinnovaVinnova [2017-02447]; DBT-VINNOVA [2020-03611]

Interface recombination in sub-mu m optoelectronics has a major detrimental impact on devices' performance, showing the need for tailored passivation strategies to reach a technological boost. In this article, SiO$_x$ passivation based substrates were developed and integrated into ultrathin Cu(In,Ga)Se$_2$ (CIGS) solar cells. This article aims to understand the impact of a passivation strategy, which uses several SiO$_x$ layer thicknesses (3, 8, and 25 nm) integrated into high-performance substrates (HPS). The experimental study is complemented with 3-D lumerical finite-difference time-domain and 2-D Silvaco ATLAS optical and electrical simulations, respectively, to perform a decoupling of optical and electronic gains, allowing for a deep discussion on the impact of the SiO$_x$ layer thickness in the CIGS solar cell performance. This article shows that as the passivation layer thickness increases, a rise in parasitic losses is observed. Hence, a balance between beneficial passivation and optical effects with harmful architectural constraints defines a threshold thickness to attain the best solar cell performance. Analyzing their electrical parameters, the 8-nm novel SiO$_x$ based substrate achieved a light to power conversion efficiency value of 13.2%, a 1.3% absolute improvement over the conventional Mo substrate (without SiO$_x$).

OBJECTIVE This study aims to compare non-invasive real-time optical imaging and measuring methods for the quantification of microcirculation of organs during normothermic machine perfusion (NMP). This could help to determine which method is most promising for further development towards a clinical objective transplantability assessment tool. BACKGROUND In organ transplantation, microvascular damage due to an ischemic period and reperfusion is inevitable. Evidence suggests a correlation between microcirculation during reperfusion and post-implantation organ functionality. Optical methods can measure the organ microcirculation in real-time in-vivo. Laser Speckle Contrast Imaging (LSCI), Near Infrared Spectroscopy (NIRS) and Sidestream Dark Field Imaging (SDFI) were evaluated on this purpose during NMP of porcine slaughterhouse kidneys. METHODS An experiment consisting of steady-state NMP and intervention phases was designed. In the intervention phases, the pressure control was decreased and increased in steps and a main branch of the arteria renalis was clamped for three periods with each a consecutive reperfusion phase. Moreover, conventional functionality assessments, such as perfusate analysis, were performed. The microcirculations of the kidneys were imaged or measured with one of the optical methods throughout the experiment. RESULTS Consistent responses to the interventions were observed in the LSCI signals. Moreover, some significant correlation was found between these signals and conventional functionality assessments. The signals measured with NIRS showed some trend in response to the interventions. However, these were not as consistent as in the LSCI signals. SDFI recordings were blurred and did not show any response to the interventions. CONCLUSIONS From comprehension of the methods by (semi-)objective metrics, it can be concluded that LSCI is the most promising method for further development towards a cl, Technical Medicine

Functional 2-photon microscopy is a key technology for imaging neuronal activity. The recorded image sequences, however, can contain non-rigid movement artifacts which requires high-accuracy movement correction. Variational optical flow (OF) estimation is a group of methods for motion analysis with established performance in many computer vision areas. However, it has yet to be adapted to the statistics of 2-photon neuroimaging data. In this work, we present the motion compensation method Flow-Registration that outperforms previous alignment tools and allows to align and reconstruct even low signal-to-noise ratio 2-photon imaging data and is able to compensate high-divergence displacements during local drug injections. The method is based on statistics of such data and integrates previous advances in variational OF estimation. Our method is available as an easy-to-use ImageJ/FIJI plugin as well as a MATLAB toolbox with modular, object oriented file IO, native multi-channel support and compatibility with existing 2-photon imaging suites., source:https://onlinelibrary.wiley.com/doi/10.1002/jbio.202100330

Resting-state functional connectivity has revolutionized neuroimaging in the past 20 years and helped us gain insights into the functional organization of the human brain. Although most studies use functional magnetic resonance imaging (fMRI) for studying functional connectivity, functional near-infrared spectroscopy (fNIRS) has become increasingly popular due to its advantages over fMRI. However, fNIRS data is often corrupted by systemic physiology, extracerebral signal contamination, and motion artifacts which limit its widespread use. These noise sources in resting-state fNIRS data often violate the assumptions of the linear models used to model functional connectivity and may lead to incorrect inferences about statistical significance. This dissertation aims to examine the impact of these noise sources in resting-state fNIRS data and proposes preprocessing strategies and connectivity models that ameliorate their effects and improve the statistical validity of functional connectivity analysis. Chapter 2 examines multiple analysis pipelines using simulated resting-state fNIRS data to find the best strategies to correct for the effects of global systemic physiology on resting-state functional connectivity. Our results indicate that pre-filtering using principal components extracted from short-separation fNIRS channels as part of a partial correlation model was most effective in reducing spurious correlations due to shared systemic physiology. Given the high temporal resolution of fNIRS, modeling the lagged relationships between brain regions is crucial in understanding the flow of information over time. Hence, we also explored methods to model these lagged relationships while controlling for the effects of noise using multivariate Granger causality analysis. Chapter 3 surveys some strategies and techniques that have been proposed to correct for temporal autocorrelation, systemic physiology, and motion artifacts. Together, these two chapters highlight the challenges

Neural circuit function depends on the pattern of synaptic connections between neurons and the strength of those connections. Synaptic strength is determined by both postsynaptic sensitivity to neurotransmitter and the presynaptic probability of action potential evoked transmitter release (Pr). Whereas morphology and neurotransmitter receptor number indicate postsynaptic sensitivity, presynaptic indicators and the mechanism that sets Pr remain to be defined. To address this, we developed QuaSOR, a super-resolution method for determining Pr from quantal synaptic transmission imaging at hundreds of glutamatergic synapses at a time. We mapped the Pr onto super-resolution 3D molecular reconstructions of the presynaptic active zones (AZs) of the same synapses at the Drosophila larval neuromuscular junction (NMJ). We find that Pr varies greatly between synapses made by a single axon, quantify the contribution of key AZ proteins to Pr diversity and find that one of these, Complexin, suppresses spontaneous and evoked transmission differentially, thereby generating a spatial and quantitative mismatch between release modes. Transmission is thus regulated by the balance and nanoscale distribution of release-enhancing and suppressing presynaptic proteins to generate high signal-to-noise evoked transmission.

Mapping the localization of multiple proteins in their native three-dimensional (3D) context would be useful across many areas of biomedicine, but multiplexed fluorescence imaging has limited intrinsic multiplexing capability, and most methods for increasing multiplexity can only be applied to thin samples (<100 µm). Here, we harness the narrow spectrum of Raman spectroscopy and introduce Raman dye imaging and tissue clearing (RADIANT), an optical method that is capable of imaging multiple targets in thick samples in one shot. We expanded the range of suitable bioorthogonal Raman dyes and developed a tissue-clearing strategy for them (Raman 3D imaging of solvent-cleared organs (rDISCO)). We applied RADIANT to image up to 11 targets in millimeter-thick brain slices, extending the imaging depth 10- to 100-fold compared to prior multiplexed protein imaging methods. We showcased the utility of RADIANT in extracting systems information, including region-specific correlation networks and their topology in cerebellum development. RADIANT will facilitate the exploration of the intricate 3D protein interactions in complex systems.

Neurophotonics was launched in 2014 coinciding with the launch of the BRAIN Initiative focused on development of technologies for advancement of neuroscience. For the last seven years, Neurophotonics' agenda has been well aligned with this focus on neurotechnologies featuring new optical methods and tools applicable to brain studies. While the BRAIN Initiative 2.0 is pivoting towards applications of these novel tools in the quest to understand the brain, this status report reviews an extensive and diverse toolkit of novel methods to explore brain function that have emerged from the BRAIN Initiative and related large-scale efforts for measurement and manipulation of brain structure and function. Here, we focus on neurophotonic tools mostly applicable to animal studies. A companion report, scheduled to appear later this year, will cover diffuse optical imaging methods applicable to noninvasive human studies. For each domain, we outline the current state-of-the-art of the respective technologies, identify the areas where innovation is needed, and provide an outlook for the future directions.

Nanomedicine involves the use of nanotechnology for clinical applications and holds promise to improve treatments. Recent developments offer new hope for cancer detection, prevention and treatment; however, being a heterogenous disorder, cancer calls for a more targeted treatment approach. Personalized Medicine (PM) aims to revolutionize cancer therapy by matching the most effective treatment to individual patients. Nanotheranostics comprise a combination of therapy and diagnostic imaging incorporated in a nanosystem and are developed to fulfill the promise of PM by helping in the selection of treatments, the objective monitoring of response and the planning of follow-up therapy. Although well-established imaging techniques, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET) and Single-Photon Emission Computed Tomography (SPECT), are primarily used in the development of theranostics, Optical Imaging (OI) offers some advantages, such as high sensitivity, spatial and temporal resolution and less invasiveness. Additionally, it allows for multiplexing, using multi-color imaging and DNA barcoding, which further aids in the development of personalized treatments. Recent advances have also given rise to techniques permitting better penetration, opening new doors for OI-guided nanotheranostics. In this review, we describe in detail these recent advances that may be used to design and develop efficient and specific nanotheranostics for personalized cancer drug delivery.

Oral cancer is a cancer type that is widely prevalent in low-and middle-income countries with a high mortality rate, and poor quality of life for patients after treatment. Early treatment of cancer increases patient survival, improves quality of life and results in less morbidity and a better prognosis. To reach this goal, early detection of malignancies using technologies that can be used in remote and low resource areas is desirable. Such technologies should be affordable, accurate, and easy to use and interpret. This review surveys different technologies that have the potentials of implementation in primary health and general dental practice, considering global perspectives and with a focus on the population in India, where oral cancer is highly prevalent. The technologies reviewed include both sample-based methods, such as saliva and blood analysis and brush biopsy, and more direct screening of the oral cavity including fluorescence, Raman techniques, and optical coherence tomography. Digitalisation, followed by automated artificial intelligence based analysis, are key elements in facilitating wide access to these technologies, to non-specialist personnel and in rural areas, increasing quality and objectivity of the analysis while simultaneously reducing the labour and need for highly trained specialists., Funding: Linkoping University; Folktandvarden Stockholms la AB [7071]; Folktandvarden Region Dalarna, forskningsstiftelsen Folktandvarden Dalarna; VinnovaVinnova [2017-02447]; DBT-VINNOVA [2020-03611]

Oral cancer is a cancer type that is widely prevalent in low-and middle-income countries with a high mortality rate, and poor quality of life for patients after treatment. Early treatment of cancer increases patient survival, improves quality of life and results in less morbidity and a better prognosis. To reach this goal, early detection of malignancies using technologies that can be used in remote and low resource areas is desirable. Such technologies should be affordable, accurate, and easy to use and interpret. This review surveys different technologies that have the potentials of implementation in primary health and general dental practice, considering global perspectives and with a focus on the population in India, where oral cancer is highly prevalent. The technologies reviewed include both sample-based methods, such as saliva and blood analysis and brush biopsy, and more direct screening of the oral cavity including fluorescence, Raman techniques, and optical coherence tomography. Digitalisation, followed by automated artificial intelligence based analysis, are key elements in facilitating wide access to these technologies, to non-specialist personnel and in rural areas, increasing quality and objectivity of the analysis while simultaneously reducing the labour and need for highly trained specialists., Funding: Linkoping University; Folktandvarden Stockholms la AB [7071]; Folktandvarden Region Dalarna, forskningsstiftelsen Folktandvarden Dalarna; VinnovaVinnova [2017-02447]; DBT-VINNOVA [2020-03611]