Digital Holography and 3D Imaging 2023: introduction to the joint feature issue in Applied Optics and Journal of the Optical Society of America A.

The Optica Topical Meeting on Digital Holography and 3D Imaging (DH) was held 14-17 August 2023 in Boston, Massachusetts. The meeting was organized co-jointly with the Optica Imaging Congress. Feature issues based on the DH meeting series have been released by Applied Optics (AO) since 2007. Since 2017, AO and the Journal of the Optical Society of America A (JOSA A) have presented a feature issue in each journal. This feature issues includes 17 papers in AO and 9 in JOSA A. Together they cover a large range of topics, reflecting the rapidly expanding techniques and applications of digital holography and 3D imaging. The upcoming DH Conference (DH 2024) will be held from 3 to 6 June in Paestum, Italy.

Digital Holography and 3D Imaging: introduction to the joint feature issue in Applied Optics and Journal of the Optical Society of America A.

The Optica Topical Meeting on Digital Holography and 3D Imaging (DH) was held 14-17 August 2023 in Boston, Massachusetts. The meeting was organized co-jointly with the Optica Imaging Congress. Feature issues based on the DH meeting series have been released by Applied Optics (AO) since 2007. Since 2017, AO and the Journal of the Optical Society of America A (JOSA A) have presented a feature issue in each journal. This feature issues includes 17 papers in AO and 9 in JOSA A. Together they cover a large range of topics, reflecting the rapidly expanding techniques and applications of digital holography and 3D imaging. The upcoming DH Conference (DH 2024) will be held from 3 to 6 June in Paestum, Italy.

Safety and Effectiveness of Laser or Intense Pulsed Light Treatment for Early Surgical Scar: A Systematic Review and Meta-analysis.

OBJECTIVE: We aimed to investigate the safety and efficacy of laser or intense pulsed light therapy for early treatment of surgical scar. METHODS: A literature search was conducted for relevant prospective, randomized controlled trials published in PubMed, Embase, Web of Science, Cochrane Library, CNKI, WanFang Database, and VTTMS between January 2006 and January 2022. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist was used to extract literature data. The risk of bias was assessed by RevMan. Safety was assessed based on the presence of serious adverse reactions (blisters, infections, burns above the second degree), while effectiveness was assessed using the Vancouver Score Scale. RESULTS: 1512 related articles were preliminarily retrieved, including 1211 English articles and 301 Chinese articles. According to the inclusion criteria and exclusion criteria, 12 articles were selected for this analysis. In total, 475 patients were included (laser group, 238; control group, 236). All studies confirmed that the laser group was superior to the control group. In the subgroup analysis of 7 articles, the standardized mean difference was 1.99 (P = 0.0001). CONCLUSIONS: This meta-analysis demonstrates that laser or intense pulsed light therapy is a safe and effective approach for early surgical scar treatment, resulting in improved scar appearance and minimal adverse reactions. LEVEL OF EVIDENCE I: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Optimizing for In-Memory Deep Learning With Emerging Memory Technology.

In-memory deep learning executes neural network models where they are stored, thus avoiding long-distance communication between memory and computation units, resulting in considerable savings in energy and time. In-memory deep learning has already demonstrated orders of magnitude higher performance density and energy efficiency. The use of emerging memory technology (EMT) promises to increase density, energy, and performance even further. However, EMT is intrinsically unstable, resulting in random data read fluctuations. This can translate to nonnegligible accuracy loss, potentially nullifying the gains. In this article, we propose three optimization techniques that can mathematically overcome the instability problem of EMT. They can improve the accuracy of the in-memory deep learning model while maximizing its energy efficiency. Experiments show that our solution can fully recover most models' state-of-the-art (SOTA) accuracy and achieves at least an order of magnitude higher energy efficiency than the SOTA.

Machine learning for detection of 3D features using sparse x-ray tomographic reconstruction.

In many inertial confinement fusion (ICF) experiments, the neutron yield and other parameters cannot be completely accounted for with one and two dimensional models. This discrepancy suggests that there are three dimensional effects that may be significant. Sources of these effects include defects in the shells and defects in shell interfaces, the fill tube of the capsule, and the joint feature in double shell targets. Due to their ability to penetrate materials, x rays are used to capture the internal structure of objects. Methods such as computational tomography use x-ray radiographs from hundreds of projections, in order to reconstruct a three dimensional model of the object. In experimental environments, such as the National Ignition Facility and Omega-60, the availability of these views is scarce, and in many cases only consists of a single line of sight. Mathematical reconstruction of a 3D object from sparse views is an ill-posed inverse problem. These types of problems are typically solved by utilizing prior information. Neural networks have been used for the task of 3D reconstruction as they are capable of encoding and leveraging this prior information. We utilize half a dozen, different convolutional neural networks to produce different 3D representations of ICF implosions from the experimental data. Deep supervision is utilized to train a neural network to produce high-resolution reconstructions. These representations are used to track 3D features of the capsules, such as the ablator, inner shell, and the joint between shell hemispheres. Machine learning, supplemented by different priors, is a promising method for 3D reconstructions in ICF and x-ray radiography, in general.

Facile construction of agar-based fire-resistant aerogels: A synergistic strategy via in situ generations of magnesium hydroxide and cross-linked Ca-alginate.

Biomass-based aerogel materials have many advantages, such as low thermal conductivity and non-toxicity. These materials are environmentally friendly and have broad development potential in the fields of packaging, cushioning and green building insulation. However, defects, such as low mechanical strength and poor fire safety, greatly limit the application of these materials. In this work, the agar/polyvinyl alcohol composite aerogel modified by the magnesium hydroxide (MH)/sodium alginate (SA) composite flame retardant system was developed by using a freeze-dried technology and the strategy of in-situ generation of MH and crosslinking of SA. The results showed that the MH/SA dramatically enhanced the mechanical and thermal stability of the composites. The compression modulus of AP-M35S15 was 2.37 MPa, which was 152.13 % higher than that of AP-M50. The limiting oxygen index value of AP-M35S15 was 34.1 % and reached V-0 level in the vertical burning test, which was better than those of the samples with a single MH effect. The cone calorimetric test showed that the MH/SA composite flame retardant system performed better in extending the ignition time, slowing down the heat release rate and reducing the total heat release and had a more complete dense carbon structure after burning.

Research Progress of Three-Dimensional Bioprinting Artificial Cardiac Tissue.

Cardiovascular disease is one of the main diseases that endanger human life and health, and heart failure often occurs when the cardiovascular disease develops to the end-stage. Heart transplantation is the most effective treatment. However, there has always been a shortage of living heart organs. With the development of regenerative medicine, researchers have turned to bioprinting technology that can build tissues and organs in vitro. A large number of relevant literature on three-dimensional (3D) bioprinted hearts were searched and screened in Google Scholar. 3D bioprinting technology can accurately print biomaterials containing living cells into 3D functional living tissues, providing a feasible solution to the shortage of transplantable organs. As one of the most important organs in the human body, the research on 3D bioprinting of the heart has currently become a hot topic. This paper briefly overviews 3D bioprinting technology and the progress in bioprinting cardiac tissue. It is believed that in the future, bio-printed hearts will become a reality, making a new way of providing artificial organs for heart transplantation.

Efficient Spiking Neural Networks With Radix Encoding.

Spiking neural networks (SNNs) have advantages in latency and energy efficiency over traditional artificial neural networks (ANNs) due to their event-driven computation mechanism and the replacement of energy-consuming weight multiplication with addition. However, to achieve high accuracy, it usually requires long spike trains to ensure accuracy, usually more than 1000 time steps. This offsets the computation efficiency brought by SNNs because a longer spike train means a larger number of operations and larger latency. In this article, we propose a radix-encoded SNN, which has ultrashort spike trains. Specifically, it is able to use less than six time steps to achieve even higher accuracy than its traditional counterpart. We also develop a method to fit our radix encoding technique into the ANN-to-SNN conversion approach so that we can train radix-encoded SNNs more efficiently on mature platforms and hardware. Experiments show that our radix encoding can achieve 25 × improvement in latency and 1.7% improvement in accuracy compared to the state-of-the-art method using the VGG-16 network on the CIFAR-10 dataset.

Structural and Optical Properties of High Entropy (La,Lu,Y,Gd,Ce)AlO3 Perovskite Thin Films.

Mixtures of Ce-doped rare-earth aluminum perovskites are drawing a significant amount of attention as potential scintillating devices. However, the synthesis of complex perovskite systems leads to many challenges. Designing the A-site cations with an equiatomic ratio allows for the stabilization of a single-crystal phase driven by an entropic regime. This work describes the synthesis of a highly epitaxial thin film of configurationally disordered rare-earth aluminum perovskite oxide (La0.2 Lu0.2 Y0.2 Gd0.2 Ce0.2 )AlO3 and characterizes the structural and optical properties. The thin films exhibit three equivalent epitaxial domains having an orthorhombic structure resulting from monoclinic distortion of the perovskite cubic cell. An excitation of 286.5 nm from Gd3+ and energy transfer to Ce3+ with 405 nm emission are observed, which represents the potential for high-energy conversion. These experimental results also offer the pathway to tunable optical properties of high-entropy rare-earth epitaxial perovskite films for a range of applications.

Persistent hiccup as one of the initial symptoms of leucine-rich glioma-inactivated-1 encephalitis: a case report.

BACKGROUND: Anti-leucine-rich glioma-inactivated 1 (LGI1) encephalitis, an autoimmune disorder, is characterized by faciobrachial dystonic seizures, epilepsy, memory deficits and altered mental status while hiccup is not commonly found in patients. CASE PRESENTATION: A 62-year-old male was presented with slurred speech, abnormal gait, faciobrachial dystonic seizures and impaired cognition. Besides, the hiccup was one of the initial symptoms. His brain magnetic resonance images (MRI) revealed multiple lesions with left caudate nucleus, putamen, insula and left hippocampus involvement. Because a diagnosis of antibody-related limbic encephalitis was suspected, studies including an autoimmune profile were done by cell-based assays. After anti-LGI1 antibodies were detected in both cerebrospinal fluid and serology, pulse methylprednisolone and intravenous immunoglobulin were started and hence hiccups disappeared along with other symptoms. CONCLUSIONS: Clinicians should be aware that persistent hiccups might be one of the initial manifestations of LGI1 subtype of voltage-gated potassium channel complex antibody associated autoimmune encephalitis.

EDCompress: Energy-Aware Model Compression for Dataflows.

Edge devices demand low energy consumption, cost, and small form factor. To efficiently deploy convolutional neural network (CNN) models on the edge device, energy-aware model compression becomes extremely important. However, existing work did not study this problem well because of the lack of considering the diversity of dataflow types in hardware architectures. In this article, we propose EDCompress (EDC), an energy-aware model compression method for various dataflows. It can effectively reduce the energy consumption of various edge devices, with different dataflow types. Considering the very nature of model compression procedures, we recast the optimization process to a multistep problem and solve it by reinforcement learning algorithms. We also propose a multidimensional multistep (MDMS) optimization method, which shows higher compressing capability than the traditional multistep method. Experiments show that EDC could improve 20x, 17x, and 26x energy efficiency in VGG-16, MobileNet, and LeNet-5 networks, respectively, with negligible loss of accuracy. EDC could also indicate the optimal dataflow type for specific neural networks in terms of energy consumption, which can guide the deployment of CNN on hardware.

Radiographic imaging and tomography.

Radiographic imaging and tomography (RadIT) come in many types such as x-ray imaging and tomography (IT), proton IT, neutron IT, muon IT, and more. We identify five RadIT themes: physics, sources, detectors, methods, and data science, which are integral parts of image interpretation and 3D tomographic reconstruction. Traditionally, RadIT has been driven by medicine, non-destructive testing, material sciences, and security applications. The latest thrusts of growth come from automation, machine vision, additive manufacturing, and virtual reality (the "metaverse"). The five RadIT themes parallel their counterparts in optical IT. Synergies among different forms of RadIT and with optical IT motivate further advances towards multi-modal IT and quantum IT.

Simulating 50 keV X-ray Photon Detection in Silicon with a Down-Conversion Layer.

Simulation results are presented that explore an innovative, new design for X-ray detection in the 20-50 keV range that is an alternative to traditional direct and indirect detection methods. Typical indirect detection using a scintillator must trade-off between absorption efficiency and spatial resolution. With a high-Z layer that down-converts incident photons on top of a silicon detector, this design has increased absorption efficiency without sacrificing spatial resolution. Simulation results elucidate the relationship between the thickness of each layer and the number of photoelectrons generated. Further, the physics behind the production of electron-hole pairs in the silicon layer is studied via a second model to shed more light on the detector's functionality. Together, the two models provide a greater understanding of this detector and reveal the potential of this novel form of X-ray detection.

Time-lens photon Doppler velocimetry (TL-PDV).

We describe a time lens (TL) to expand the dynamic range of photon Doppler velocimetry (PDV) systems. The principle and preliminary design of a TL-PDV system are explained and shown to be feasible through simulations. In a PDV system, an interferometer is used for measuring frequency shifts due to the Doppler effect from the target motion. However, the sampling rate of the electronics could limit the velocity range of a PDV system. A four-wave-mixing (FWM) TL applies a quadratic temporal phase to an optical signal within a nonlinear FWM medium (such as an integrated photonic waveguide or a highly nonlinear optical fiber). By spectrally isolating the mixing product, termed the idler, and with appropriate lengths of dispersion prior to and after this FWM TL, a temporally magnified version of the input signal is generated. Therefore, the frequency shifts of PDV can be "slowed down" with the magnification factor M of the TL. M = 1 corresponds to a regular PDV system without a TL. M = 10 has been shown to be feasible for a TL-PDV system. The use of this effect for PDV can expand the velocity measurement range and allow for the use of lower bandwidth electronics. TL-PDV will open up new avenues for various dynamic material experiments.

Billion-pixel x-ray camera (BiPC-X).

The continuing improvement in quantum efficiency (above 90% for single visible photons), reduction in noise (below 1 electron per pixel), and shrink in pixel pitch (less than 1 µm) enable billion-pixel x-ray cameras (BiPC-X) based on commercial complementary metal-oxide-semiconductor (CMOS) imaging sensors. We describe BiPC-X designs and prototype construction based on flexible tiling of commercial CMOS imaging sensors with millions of pixels. Device models are given for direct detection of low energy x rays (<10 keV) and indirect detection of higher energies using scintillators. Modified Birks's law is proposed for light yield non-proportionality in scintillators as a function of x-ray energy. Single x-ray sensitivity and spatial resolution have been validated experimentally using a laboratory x-ray source and the Argonne Advanced Photon Source. Possible applications include wide field-of-view or large x-ray aperture measurements in high-temperature plasmas, the state-of-the-art synchrotron, x-ray free electron laser, and pulsed power facilities.

Prevalence of diabetes and hypertension and their interaction effects on cardio-cerebrovascular diseases: a cross-sectional study.

BACKGROUND: Hypertension and diabetes mellitus are two of the major risk factors for cardio-cerebrovascular diseases (CVDs). Although prior studies have confirmed that the coexistence of the two can markedly increase the risk of CVDs, few studies investigated whether potential interaction effects of hypertension and diabetes can result in greater cardio-cerebrovascular damage. We aimed to investigate the prevalence of hypertension and diabetes and whether they both affect synergistically the risk of CVDs. METHODS: A cross-sectional study was conducted by using a multistage stratified random sampling among communities in Changsha City, Hunan Province. Study participants aged > = 18 years were asked to complete questionnaires and physical examinations. Multivariate logistic regression models were performed to evaluate the association of diabetes, hypertension, and their multiplicative interaction with CVDs with adjustment for potential confounders. We also evaluated additive interaction with the relative excess risk ratio (RERI), attribution percentage (AP), synergy index (SI). RESULTS: A total of 14,422 participants aged 18-98 years were collected (men = 5827, 40.7%). The prevalence was 22.7% for hypertension, 7.0% for diabetes, and 3.8% for diabetes with hypertension complication, respectively. Older age, women, higher educational level, unmarried status, obesity (central obesity) were associated with increased risk of hypertension and diabetes. We did not find significant multiplicative interaction of diabetes and hypertension on CVDs, but observed a synergistic additive interaction on coronary heart disease (SI, 1.43; 95% CI, 1.03-1.97; RERI, 1.94; 95% CI, 0.05-3.83; AP, 0.26; 95% CI, 0.06-0.46). CONCLUSIONS: Diabetes and hypertension were found to be associated with a significantly increased risk of CVDs and a significant synergistic additive interaction of diabetes and hypertension on coronary heart disease was observed. Participants who were old, women, highly educated, unmarried, obese (central obese) had increased risk of diabetes and hypertension.

Asynchronous and Load-Balanced Union-Find for Distributed and Parallel Scientific Data Visualization and Analysis.

We present a novel distributed union-find algorithm that features asynchronous parallelism and k-d tree based load balancing for scalable visualization and analysis of scientific data. Applications of union-find include level set extraction and critical point tracking, but distributed union-find can suffer from high synchronization costs and imbalanced workloads across parallel processes. In this study, we prove that global synchronizations in existing distributed union-find can be eliminated without changing final results, allowing overlapped communications and computations for scalable processing. We also use a k-d tree decomposition to redistribute inputs, in order to improve workload balancing. We benchmark the scalability of our algorithm with up to 1,024 processes using both synthetic and application data. We demonstrate the use of our algorithm in critical point tracking and super-level set extraction with high-speed imaging experiments and fusion plasma simulations, respectively.

Neural network for 3D inertial confinement fusion shell reconstruction from single radiographs.

In inertial confinement fusion (ICF), x-ray radiography is a critical diagnostic for measuring implosion dynamics, which contain rich three-dimensional (3D) information. Traditional methods for reconstructing 3D volumes from 2D radiographs, such as filtered backprojection, require radiographs from at least two different angles or lines of sight (LOS). In ICF experiments, the space for diagnostics is limited, and cameras that can operate on fast timescales are expensive to implement, limiting the number of projections that can be acquired. To improve the imaging quality as a result of this limitation, convolutional neural networks (CNNs) have recently been shown to be capable of producing 3D models from visible light images or medical x-ray images rendered by volumetric computed tomography. We propose a CNN to reconstruct 3D ICF spherical shells from single radiographs. We also examine the sensitivity of the 3D reconstruction to different illumination models using preprocessing techniques such as pseudo-flatfielding. To resolve the issue of the lack of 3D supervision, we show that training the CNN utilizing synthetic radiographs produced by known simulation methods allows for reconstruction of experimental data as long as the experimental data are similar to the synthetic data. We also show that the CNN allows for 3D reconstruction of shells that possess low mode asymmetries. Further comparisons of the 3D reconstructions with direct multiple LOS measurements are justified.

Two-photon X-ray ghost microscope.

This article presents a non-classical imaging mechanism that produces a diffraction-limited and magnified ghost image of the internal structure of an object through the measurement of intensity fluctuation correlation formed by two-photon interference. In principle, the lensless X-ray ghost imaging mechanism may achieve a spatial resolution determined by the wavelength and the angular diameter of the X-ray source, ∼λ/Δθs, with possible reduction caused by additional optics. In addition, it has the ability to image select "slices" deep within an object, which can be used for constructing 3D view of its internal structure.

Role of TG2 and TGF-ß1 in the pathogenesis of human breast cancer.

The present study analyzed the role of transforming growth factor-ß1 (TGF-ß1) and tissue transglutaminase (TG2) in breast cancer, as well as their protein levels in MCF-7 cells treated with cisplatin. In addition, the present study investigated the effects of TG2 and TGF-ß1 in MCF-7 cells following TGF-ß1 and TG2 inhibition or TGF-ß1 induction. The protein levels of TG2 and TGF-ß1 in breast cancer tissues and in MCF-7 cells treated with cisplatin, TG2 and TGF-ß1 inhibitors or 10 ng/ml TGF-ß1 were analyzed by immunohistochemical staining, immunofluorescence and western blotting. The results revealed that the expression levels of TG2 and TGF-ß1 in breast cancer tissues were significantly higher compared with those in paracancerous tissues. The fluorescence intensity of TG2 and TGF-ß1 in MCF-7 cells treated with cisplatin was lower compared with that in untreated MCF-7 cells. Using bioinformatics analysis, the present study predicted that TGF-ß1 may be associated with TG2. In addition, the expression levels of TGF-ß1 and TG2 in MCF-7 cells treated with inhibitors of TGF-ß1 and TG2 were lower compared with those in untreated MCF-7 cells. By contrast, the expression levels of TGF-ß1 and TG2 in MCF-7 cells treated with TGF-ß1 were higher compared with those in untreated MCF-7 cells. Therefore, the present study demonstrated that TGF-ß1 and TG2 may serve an important role in breast cancer tissues and in MCF-7 cells. In addition, it was revealed that TG2 and TGF-ß1 may have a synergistic role in MCF-7 cells.