Nonlinear processing with linear optics.

Deep neural networks have achieved remarkable breakthroughs by leveraging multiple layers of data processing to extract hidden representations, albeit at the cost of large electronic computing power. To enhance energy efficiency and speed, the optical implementation of neural networks aims to harness the advantages of optical bandwidth and the energy efficiency of optical interconnections. In the absence of low-power optical nonlinearities, the challenge in the implementation of multilayer optical networks lies in realizing multiple optical layers without resorting to electronic components. Here we present a novel framework that uses multiple scattering, and which is capable of synthesizing programmable linear and nonlinear transformations concurrently at low optical power by leveraging the nonlinear relationship between the scattering potential, represented by data, and the scattered field. Theoretical and experimental investigations show that repeating the data by multiple scattering enables nonlinear optical computing with low-power continuous-wave light. Moreover, we empirically find that scaling of this optical framework follows a power law.

3Din vitromodeling of the exocrine pancreatic unit using tomographic volumetric bioprinting.

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, a leading cause of cancer-related deaths globally. Initial lesions of PDAC develop within the exocrine pancreas' functional units, with tumor progression driven by interactions between PDAC and stromal cells. Effective therapies require anatomically and functionally relevantin vitrohuman models of the pancreatic cancer microenvironment. We employed tomographic volumetric bioprinting, a novel biofabrication method, to create human fibroblast-laden constructs mimicking the tubuloacinar structures of the exocrine pancreas. Human pancreatic ductal epithelial (HPDE) cells overexpressing the KRAS oncogene (HPDE-KRAS) were seeded in the multiacinar cavity to replicate pathological tissue. HPDE cell growth and organization within the structure were assessed, demonstrating the formation of a thin epithelium covering the acini inner surfaces. Immunofluorescence assays showed significantly higher alpha smooth muscle actin (α-SMA) vs. F-actin expression in fibroblasts co-cultured with cancerous versus wild-type HPDE cells. Additionally,α-SMA expression increased over time and was higher in fibroblasts closer to HPDE cells. Elevated interleukin (IL)-6 levels were quantified in supernatants from co-cultures of stromal and HPDE-KRAS cells. These findings align with inflamed tumor-associated myofibroblast behavior, serving as relevant biomarkers to monitor early disease progression and target drug efficacy. To our knowledge, this is the first demonstration of a 3D bioprinted model of exocrine pancreas that recapitulates its true 3-dimensional microanatomy and shows tumor triggered inflammation.

High-resolution adaptive optics-trans-scleral flood illumination (AO-TFI) imaging of retinal pigment epithelium (RPE) in central serous chorioretinopathy (CSCR).

This study aims to correlate adaptive optics-transscleral flood illumination (AO-TFI) images of the retinal pigment epithelium (RPE) in central serous chorioretinopathy (CSCR) with standard clinical images and compare cell morphological features with those of healthy eyes. After stitching 125 AO-TFI images acquired in CSCR eyes (including 6 active CSCR, 15 resolved CSCR, and 3 from healthy contralateral), 24 montages were correlated with blue-autofluorescence, infrared and optical coherence tomography images. All 68 AO-TFI images acquired in pathological areas exhibited significant RPE contrast changes. Among the 52 healthy areas in clinical images, AO-TFI revealed a normal RPE mosaic in 62% of the images and an altered RPE pattern in 38% of the images. Morphological features of the RPE cells were quantified in 54 AO-TFI images depicting clinically normal areas (from 12 CSCR eyes). Comparison with data from 149 AO-TFI images acquired in 33 healthy eyes revealed significantly increased morphological heterogeneity. In CSCR, AO-TFI not only enabled high-resolution imaging of outer retinal alterations, but also revealed RPE abnormalities undetectable by all other imaging modalities. Further studies are required to estimate the prognosis value of these abnormalities. Imaging of the RPE using AO-TFI holds great promise for improving our understanding of the CSCR pathogenesis.

Wave optical model for tomographic volumetric additive manufacturing.

Tomographic Volumetric Additive Manufacturing (TVAM) allows printing of mesoscopic objects within seconds or minutes. In TVAM, tomographic patterns are illuminated onto a rotating glass vial which contains a photosensitive resin. Current pattern optimization is based on a ray optical assumption which ultimately leads to limited resolution around 20 µm and varying throughout the volume of the 3D object. In this work, we introduce a rigorous wave-based optical amplitude optimization scheme for TVAM which shows that high-resolution printing is theoretically possible over the full volume. The wave optical optimization approach is based on an efficient angular spectrum method of plane waves with custom written memory efficient gradients and allows for optimization of realistic volumes for TVAM such as (100µm)3 or (10 mm)3 with 5503 voxels and 600 angles. Our simulations show that ray-optics start to produce artifacts when the desired features are 20 µm and below and more importantly, the amplitude modulated TVAM can reach sub 20 µm features when optimizing the patterns using a full wave model.

Electrowriting of SU-8 Microfibers.

As microfiber-based additive manufacturing (AM) technologies, melt electrowriting (MEW) and solution electrowriting (SEW) have demonstrated efficacy with more biomedically relevant materials. By processing SU-8 resin using MEW and SEW techniques, a material with substantially different mechanical, thermal, and optical properties than that typically processed is introduced. SU-8 polymer is temperature sensitive and requires the devising of a specific heating protocol to be properly processed. Smooth-surfaced microfibers resulted from MEW of SU8 for a short period (from 30 to 90 min), which provides the greatest control and, thus, reproducibility of the printed microfibers. This investigation explores various parameters influencing the electrowriting process, printing conditions, and post-processing to optimize the fabrication of intricate 3D structures. This work demonstrates the controlled generation of straight filaments and complex multi-layered architectures, which were characterized by brightfield, darkfield, and scanning electron microscopy (SEM). This research opens new avenues for the design and development of 3D-printed photonic systems by leveraging the properties of SU-8 after both MEW and SEW processing.

Volume holograms with linear diffraction efficiency relation by (3 + 1)D printing.

We demonstrate the fabrication of volume holograms using two-photon polymerization with dynamic control of light exposure. We refer to our method as (3 + 1)D printing. Volume holograms that are recorded by interfering reference and signal beams have a diffraction efficiency relation that is inversely proportional to the square of the number of superimposed holograms. By using (3 + 1)D printing for fabrication, the refractive index of each voxel is created independently and thus, by digitally filtering the undesired interference terms, the diffraction efficiency is now inversely proportional to the number of multiplexed gratings. We experimentally demonstrated this linear dependence by recording M = 50 volume gratings. To the best of our knowledge, this is the first experimental demonstration of distributed volume holograms that overcome the 1/M2 limit.

An actor-model framework for visual sensory encoding.

A fundamental challenge in neuroengineering is determining a proper artificial input to a sensory system that yields the desired perception. In neuroprosthetics, this process is known as artificial sensory encoding, and it holds a crucial role in prosthetic devices restoring sensory perception in individuals with disabilities. For example, in visual prostheses, one key aspect of artificial image encoding is to downsample images captured by a camera to a size matching the number of inputs and resolution of the prosthesis. Here, we show that downsampling an image using the inherent computation of the retinal network yields better performance compared to learning-free downsampling methods. We have validated a learning-based approach (actor-model framework) that exploits the signal transformation from photoreceptors to retinal ganglion cells measured in explanted mouse retinas. The actor-model framework generates downsampled images eliciting a neuronal response in-silico and ex-vivo with higher neuronal reliability than the one produced by a learning-free approach. During the learning process, the actor network learns to optimize contrast and the kernel's weights. This methodological approach might guide future artificial image encoding strategies for visual prostheses. Ultimately, this framework could be applicable for encoding strategies in other sensory prostheses such as cochlear or limb.

Forward-forward training of an optical neural network.

Neural networks (NNs) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementations. Optics-based platforms, using technologies such as silicon photonics and spatial light modulators, offer promising avenues for achieving this goal. However, training multiple programmable layers together with these physical systems poses challenges, as they are difficult to fully characterize and describe with differentiable functions, hindering the use of error backpropagation algorithm. The recently introduced forward-forward algorithm (FFA) eliminates the need for perfect characterization of the physical learning system and shows promise for efficient training with large numbers of programmable parameters. The FFA does not require backpropagating an error signal to update the weights, rather the weights are updated by only sending information in one direction. The local loss function for each set of trainable weights enables low-power analog hardware implementations without resorting to metaheuristic algorithms or reinforcement learning. In this paper, we present an experiment utilizing multimode nonlinear wave propagation in an optical fiber demonstrating the feasibility of the FFA approach using an optical system. The results show that incorporating optical transforms in multilayer NN architectures trained with the FFA can lead to performance improvements, even with a relatively small number of trainable weights. The proposed method offers a new path to the challenge of training optical NNs and provides insights into leveraging physical transformations for enhancing the NN performance.

A review of materials used in tomographic volumetric additive manufacturing.

Volumetric additive manufacturing is a novel fabrication method allowing rapid, freeform, layer-less 3D printing. Analogous to computer tomography (CT), the method projects dynamic light patterns into a rotating vat of photosensitive resin. These light patterns build up a three-dimensional energy dose within the photosensitive resin, solidifying the volume of the desired object within seconds. Departing from established sequential fabrication methods like stereolithography or digital light printing, volumetric additive manufacturing offers new opportunities for the materials that can be used for printing. These include viscous acrylates and elastomers, epoxies (and orthogonal epoxy-acrylate formulations with spatially controlled stiffness) formulations, tunable stiffness thiol-enes and shape memory foams, polymer derived ceramics, silica-nanocomposite based glass, and gelatin-based hydrogels for cell-laden biofabrication. Here we review these materials, highlight the challenges to adapt them to volumetric additive manufacturing, and discuss the perspectives they present. Supplementary Information: The online version contains supplementary material available at10.1557/s43579-023-00447-x.

In Vivo Retinal Pigment Epithelium Imaging using Transscleral Optical Imaging in Healthy Eyes.

Objective: To image healthy retinal pigment epithelial (RPE) cells in vivo using Transscleral OPtical Imaging (TOPI) and to analyze statistics of RPE cell features as a function of age, axial length (AL), and eccentricity. Design: Single-center, exploratory, prospective, and descriptive clinical study. Participants: Forty-nine eyes (AL: 24.03 ± 0.93 mm; range: 21.9-26.7 mm) from 29 participants aged 21 to 70 years (37.1 ± 13.3 years; 19 men, 10 women). Methods: Retinal images, including fundus photography and spectral-domain OCT, AL, and refractive error measurements were collected at baseline. For each eye, 6 high-resolution RPE images were acquired using TOPI at different locations, one of them being imaged 5 times to evaluate the repeatability of the method. Follow-up ophthalmic examination was repeated 1 to 3 weeks after TOPI to assess safety. Retinal pigment epithelial images were analyzed with a custom automated software to extract cell parameters. Statistical analysis of the selected high-contrast images included calculation of coefficient of variation (CoV) for each feature at each repetition and Spearman and Mann-Whitney tests to investigate the relationship between cell features and eye and subject characteristics. Main Outcome Measures: Retinal pigment epithelial cell features: density, area, center-to-center spacing, number of neighbors, circularity, elongation, solidity, and border distance CoV. Results: Macular RPE cell features were extracted from TOPI images at an eccentricity of 1.6° to 16.3° from the fovea. For each feature, the mean CoV was < 4%. Spearman test showed correlation within RPE cell features. In the perifovea, the region in which images were selected for all participants, longer AL significantly correlated with decreased RPE cell density (R Spearman, Rs = -0.746; P < 0.0001) and increased cell area (Rs = 0.668; P < 0.0001), without morphologic changes. Aging was also significantly correlated with decreased RPE density (Rs = -0.391; P = 0.036) and increased cell area (Rs = 0.454; P = 0.013). Lower circular, less symmetric, more elongated, and larger cells were observed in those > 50 years. Conclusions: The TOPI technology imaged RPE cells in vivo with a repeatability of < 4% for the CoV and was used to analyze the influence of physiologic factors on RPE cell morphometry in the perifovea of healthy volunteers. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

NIR Light-Mediated Photocuring of Adhesive Hydrogels for Noninvasive Tissue Repair via Upconversion Optogenesis.

The surgical treatments of injured soft tissues lead to further injury due to the use of sutures or the surgical routes, which need to be large enough to insert biomaterials for repair. In contrast, the use of low viscosity photopolymerizable hydrogels that can be inserted with thin needles represents a less traumatic treatment and would therefore reduce the severity of iatrogenic injury. However, the delivery of light to solidify the inserted hydrogel precursor requires a direct access to it, which is mostly invasive. To circumvent this limitation, we investigate the approach of curing the hydrogel located behind biological tissues by sending near-infrared (NIR) light through the latter, as this spectral region has the largest transmittance in biological tissues. Upconverting nanoparticles (UCNPs) are incorporated in the hydrogel precursor to convert NIR transmitted through the tissues into blue light to trigger the photopolymerization. We investigated the photopolymerization process of an adhesive hydrogel placed behind a soft tissue. Bulk polymerization was achieved with local radiation of the adhesive hydrogel through a focused light system. Thus, unlike the common methods for uniform illumination, adhesion formation was achieved with local micrometer-sized radiation of the bulky hydrogel through a gradient photopolymerization phenomenon. Nanoindentation and upright microscope analysis confirmed that the proposed approach for indirect curing of hydrogels below the tissue is a gradient photopolymerization phenomenon. Moreover, we found that the hydrogel mechanical and adhesive properties can be modulated by playing with different parameters of the system such as the NIR light power and the UCNP concentration. The proposed photopolymerization of adhesive hydrogels below the tissue opens the prospect of a minimally invasive surgical treatment of injured soft tissues.

Lock-in Raman difference spectroscopy.

Shifted Excitation Raman Difference Spectroscopy (SERDS) is a non-destructive chemical analysis method capable of removing the fluorescence background and other disturbances from the Raman spectrum, thanks to the independence of the fluorescence with respect to the small difference in excitation wavelength. The spectrum difference is computed in a post-processing step. Here, we demonstrate the use of a lock-in camera to obtain an on-line analog SERDS spectra allowing longer exposure times and no saturation, leading to an improved Signal-to-Noise Ratio (SNR) and reduced data storage. Two configurations are presented: the first one uses a single laser and can remove excitation-independent disturbances, such as ambient light; the second employs two-wavelength shifted sources and removes fluorescence background similarly to SERDS. In both cases, we experimentally extrapolate the expected SNR improvement.

Controlling Light in Scattering Materials for Volumetric Additive Manufacturing.

3D printing has revolutionized the manufacturing of volumetric components and structures in many areas. Several fully volumetric light-based techniques have been recently developed thanks to the advent of photocurable resins, promising to reach unprecedented short print time (down to a few tens of seconds) while keeping a good resolution (around 100 µm). However, these new approaches only work with homogeneous and relatively transparent resins so that the light patterns used for photo-polymerization are not scrambled along their propagation. Herein, a method that takes into account light scattering in the resin prior to computing projection patterns is proposed. Using a tomographic volumetric printer, it is experimentally demonstrated that implementation of this correction is critical when printing objects whose size exceeds the scattering mean free path. To show the broad applicability of the technique, functional objects of high print fidelity are fabricated in hard organic scattering acrylates and soft cell-laden hydrogels (at 4 million cells mL-1 ). This opens up promising perspectives in printing inside turbid materials with particular interesting applications for bioprinting cell-laden constructs.

A constrained method for lensless coherent imaging of thin samples.

Lensless inline holography can produce high-resolution images over a large field of view (FoV). In a previous work [Appl. Opt.60, B38 (2021)APOPAI0003-693510.1364/AO.414976], we showed that (i) the actual FoV can be extrapolated outside of the camera FoV and (ii) the effective resolution of the setup can be several times higher than the resolution of the camera. In this paper, we present a reconstruction method to recover high resolution with an extrapolated FoV image of the phase and the amplitude of a sample from aliased intensity measurements taken at a lower resolution.

Volumetric Bioprinting of Organoids and Optically Tuned Hydrogels to Build Liver-Like Metabolic Biofactories.

Organ- and tissue-level biological functions are intimately linked to microscale cell-cell interactions and to the overarching tissue architecture. Together, biofabrication and organoid technologies offer the unique potential to engineer multi-scale living constructs, with cellular microenvironments formed by stem cell self-assembled structures embedded in customizable bioprinted geometries. This study introduces the volumetric bioprinting of complex organoid-laden constructs, which capture key functions of the human liver. Volumetric bioprinting via optical tomography shapes organoid-laden gelatin hydrogels into complex centimeter-scale 3D structures in under 20 s. Optically tuned bioresins enable refractive index matching of specific intracellular structures, countering the disruptive impact of cell-mediated light scattering on printing resolution. This layerless, nozzle-free technique poses no harmful mechanical stresses on organoids, resulting in superior viability and morphology preservation post-printing. Bioprinted organoids undergo hepatocytic differentiation showing albumin synthesis, liver-specific enzyme activity, and remarkably acquired native-like polarization. Organoids embedded within low stiffness gelatins (<2 kPa) are bioprinted into mathematically defined lattices with varying degrees of pore network tortuosity, and cultured under perfusion. These structures act as metabolic biofactories in which liver-specific ammonia detoxification can be enhanced by the architectural profile of the constructs. This technology opens up new possibilities for regenerative medicine and personalized drug testing.

Degradation study on molecules released from laser-based jet injector.

Development of needle-free methods to administer injectable therapeutics has been researched for a few decades. We focused our attention on a laser-based jet injection technique where the liquid-jet actuation mechanism is based on optical cavitation. This study investigates the potential damage to therapeutic molecules which are exposed to nanosecond laser pulses in the configuration of a compact laser-based jet injection device. Implementation of a pulsed laser source at 1574 nm wavelength allowed us to generate jets from pure water solutions and circumvent the need to reformulate therapeutics with absorbing dyes. We performed H1-NMR analysis on exposed samples of Lidocaine and δ-Aminolevulinic acid. We made several tests with linear and plasmid DNA to assess the structural integrity and functional potency after ejection with our device. The tests showed no significant degradation or detectable side products, which is promising for further development and eventually clinical applications.

An Intrinsically-Adhesive Family of Injectable and Photo-Curable Hydrogels with Functional Physicochemical Performance for Regenerative Medicine.

Attaching hydrogels to soft internal tissues is crucial for the development of various biomedical devices. Tough sticky hydrogel patches present high adhesion, yet with lack of injectability and the need for treatment of contacting surface. On the contrary, injectable and photo-curable hydrogels are highly attractive owing to their ease of use, flexibility of filling any shape, and their minimally invasive character, compared to their conventional preformed counterparts. Despite recent advances in material developments, a hydrogel that exhibits both proper injectability and sufficient intrinsic adhesion is yet to be demonstrated. Herein, a paradigm shift is proposed toward the design of intrinsically adhesive networks for injectable and photo-curable hydrogels. The bioinspired design strategy not only provides strong adhesive contact, but also results in a wide window of physicochemical properties. The adhesive networks are based on a family of polymeric backbones where chains are modified to be intrinsically adhesive to host tissue and simultaneously form a hydrogel network via a hybrid cross-linking mechanism. With this strategy, adhesion is achieved through a controlled synergy between the interfacial chemistry and bulk mechanical properties. The functionalities of the bioadhesives are demonstrated for various applications, such as tissue adhesives, surgical sealants, or injectable scaffolds.

Fully automated detection, segmentation, and analysis of in vivo RPE single cells.

OBJECTIVE: To develop a fully automated method of retinal pigmented epithelium (RPE) cells detection, segmentation and analysis based on in vivo cellular resolution images obtained with the transscleral optical phase imaging method (TOPI). METHODS: Fourteen TOPI-RPE images from 11 healthy individuals were analysed. The developed image processing method encompassed image filtering and normalisation, detection and removal of blood vessels, cell detection and cell membrane segmentation. The produced measures were cellular density of RPE layer, cell area, number of neighbouring cells, eccentricity, circularity and solidity. In addition, we proposed coefficient of variation (CV) of RPE cellular membrane (CMDCV) and the solidity of the RPE cell membrane-shape as new metrics for the assessment of RPE single cells. RESULTS: The observed median cellular density of the RPE layer was 3743 cells/µm2 (interquartile rate (IQR) 1687), with a median observed RPE cell area of 193 µm2 (IQR 141). The mean number of neighbouring cells was 5.22 (standard deviation (SD) 0.05) per RPE cell. The mean RPE cell eccentricity was 0.67 (SD 0.02), median circularity 0.83 (IQR 0.01), and median solidity 0.92 (IQR 0.00). The median CMDCV was 0.19 (IQR 0.02). The method is characterised by a median image processing and analysis time of 48 sec (IQR 12) per image. CONCLUSIONS: The present study provides the first fully automated quantitative assessment of human RPE single cells in vivo. The method provides a baseline for future research in the field of clinical ophthalmology, enabling characterisation and diagnostics of retinal diseases at the single-cell level.

Scalable optical learning operator.

Today's heavy machine learning tasks are fueled by large datasets. Computing is performed with power-hungry processors whose performance is ultimately limited by the data transfer to and from memory. Optics is a powerful means of communicating and processing information, and there is currently intense interest in optical information processing for realizing high-speed computations. Here we present and experimentally demonstrate an optical computing framework called scalable optical learning operator, which is based on spatiotemporal effects in multimode fibers for a range of learning tasks including classifying COVID-19 X-ray lung images, speech recognition and predicting age from images of faces. The presented framework addresses the energy scaling problem of existing systems without compromising speed. We leverage simultaneous, linear and nonlinear interaction of spatial modes as a computation engine. We numerically and experimentally show the ability of the method to execute several different tasks with accuracy comparable with a digital implementation.

Fabrication of Sub-Micron Polymer Waveguides through Two-Photon Polymerization in Polydimethylsiloxane.

Flexible ultra-compact low-loss optical waveguides play a vital role in the development of soft photonics. The search for suitable materials and innovative fabrication techniques to achieve low loss long polymer optical waveguides and interconnects has proven to be challenging. In this paper, we demonstrate the fabrication of submicron optical waveguides in polydimethylsiloxane (PDMS) using divinylbenzene (DVB) as the photopolymerizable monomer through two-photon polymerization (2PP). We show that the commercial oxime ester photoinitiator Irgacure OXE02 is suitable for triggering the DVB photopolymerization, resulting in a stable and controllable fabrication process for the fabrication of defect-free, 5-cm long waveguides. We further explore a multi-track fabrication strategy to enlarge the waveguide core size up to ~3 µm for better light confinement and reduced cross-talk. In these waveguides, we measured a refractive index contrast on the order of 0.005 and a transmission loss of 0.1 dB/cm at 710 nm wavelength.