Deep learning-based incoherent holographic camera enabling acquisition of real-world holograms for holographic streaming system.

While recent research has shown that holographic displays can represent photorealistic 3D holograms in real time, the difficulty in acquiring high-quality real-world holograms has limited the realization of holographic streaming systems. Incoherent holographic cameras, which record holograms under daylight conditions, are suitable candidates for real-world acquisition, as they prevent the safety issues associated with the use of lasers; however, these cameras are hindered by severe noise due to the optical imperfections of such systems. In this work, we develop a deep learning-based incoherent holographic camera system that can deliver visually enhanced holograms in real time. A neural network filters the noise in the captured holograms, maintaining a complex-valued hologram format throughout the whole process. Enabled by the computational efficiency of the proposed filtering strategy, we demonstrate a holographic streaming system integrating a holographic camera and holographic display, with the aim of developing the ultimate holographic ecosystem of the future.

High aspect ratio nanomaterials enable delivery of functional genetic material without DNA integration in mature plants.

Genetic engineering of plants is at the core of sustainability efforts, natural product synthesis and crop engineering. The plant cell wall is a barrier that limits the ease and throughput of exogenous biomolecule delivery to plants. Current delivery methods either suffer from host-range limitations, low transformation efficiencies, tissue damage or unavoidable DNA integration into the host genome. Here, we demonstrate efficient diffusion-based biomolecule delivery into intact plants of several species with pristine and chemically functionalized high aspect ratio nanomaterials. Efficient DNA delivery and strong protein expression without transgene integration is accomplished in Nicotiana benthamiana (Nb), Eruca sativa (arugula), Triticum aestivum (wheat) and Gossypium hirsutum (cotton) leaves and arugula protoplasts. We find that nanomaterials not only facilitate biomolecule transport into plant cells but also protect polynucleotides from nuclease degradation. Our work provides a tool for species-independent and passive delivery of genetic material, without transgene integration, into plant cells for diverse biotechnology applications.

Overexpression of serum amyloid a 1 induces depressive-like behavior in mice.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by loss of memory and cognitive abilities. In AD, amyloid ß (Aß) protein aggregates in the brain of patients, forming amyloid plaques. Aß plaques are known to be surrounded by activated microglial cells. Serum amyloid A (SAA) is elevated from several hundred to 1000-fold as part of the immune response against various injuries, including trauma, infection, and inflammation. Additionally, continuous elevation of SAA is related to the development of amyloidosis. This study was designed to identify the relationship between SAA1 and AD using liver specific SAA1 overexpressing mice (TG), because SAA1 is expressed in the liver during the acute phase. We detected exogenous SAA1 expression in the brain of TG mice. This result implies that liver-derived SAA1 migrates to the brain tissues. Thus, we confirmed that the blood brain barrier (BBB) functioned normally using Evans-blue staining and CARS. Furthermore, our results show an increase in the accumulation of the 87kDa form of Aß in TG mice compared to wild type mice (WT). Additionally, the number of microglial cells and levels of pro-inflammatory cytokines were increased. Next, we investigated the relationship between SAA1 and depression by performing social interaction tests. The results showed that TG mice have a tendency to avoid stranger mice and an impaired social recognition. In conclusion, the SAA1 TG mouse model is a valuable model to study depression.

Data consistency-driven scatter kernel optimization for x-ray cone-beam CT.

Accurate and efficient scatter correction is essential for acquisition of high-quality x-ray cone-beam CT (CBCT) images for various applications. This study was conducted to demonstrate the feasibility of using the data consistency condition (DCC) as a criterion for scatter kernel optimization in scatter deconvolution methods in CBCT. As in CBCT, data consistency in the mid-plane is primarily challenged by scatter, we utilized data consistency to confirm the degree of scatter correction and to steer the update in iterative kernel optimization. By means of the parallel-beam DCC via fan-parallel rebinning, we iteratively optimized the scatter kernel parameters, using a particle swarm optimization algorithm for its computational efficiency and excellent convergence. The proposed method was validated by a simulation study using the XCAT numerical phantom and also by experimental studies using the ACS head phantom and the pelvic part of the Rando phantom. The results showed that the proposed method can effectively improve the accuracy of deconvolution-based scatter correction. Quantitative assessments of image quality parameters such as contrast and structure similarity (SSIM) revealed that the optimally selected scatter kernel improves the contrast of scatter-free images by up to 99.5%, 94.4%, and 84.4%, and of the SSIM in an XCAT study, an ACS head phantom study, and a pelvis phantom study by up to 96.7%, 90.5%, and 87.8%, respectively. The proposed method can achieve accurate and efficient scatter correction from a single cone-beam scan without need of any auxiliary hardware or additional experimentation.

Colloidal polymers from dipolar assembly of cobalt-tipped CdSe@CdS nanorods.

The synthesis of a modular colloidal polymer system based on the dipolar assembly of CdSe@CdS nanorods functionalized with a single cobalt nanoparticle "tip" (CoNP-tip) is reported. These heterostructured nanorods spontaneously self-assembled via magnetic dipolar associations of the cobalt domains. In these assemblies, CdSe@CdS nanorods were carried as densely grafted side chain groups along the dipolar NP chain to form bottlebrush-type colloidal polymers. Nanorod side chains strongly affected the conformation of individual colloidal polymer bottlebrush chains and the morphology of thin films. Dipolar CoNP-tipped nanorods were then used as "colloidal monomers" to form mesoscopic assemblies reminiscent of traditional copolymers possessing segmented and statistical compositions. Investigation of the phase behavior of colloidal polymer blends revealed the formation of mesoscopic phase separated morphologies from segmented colloidal copolymers. These studies demonstrated the ability to control colloidal polymer composition and morphology in a manner observed for classical polymer systems by synthetic control of heterostructured nanorod structure and harnessing interparticle dipolar associations.

Information-theoretic discrepancy based iterative reconstructions (IDIR) for polychromatic x-ray tomography.

PURPOSE: X-ray photons generated from a typical x-ray source for clinical applications exhibit a broad range of wavelengths, and the interactions between individual particles and biological substances depend on particles' energy levels. Most existing reconstruction methods for transmission tomography, however, neglect this polychromatic nature of measurements and rely on the monochromatic approximation. In this study, we developed a new family of iterative methods that incorporates the exact polychromatic model into tomographic image recovery, which improves the accuracy and quality of reconstruction. METHODS: The generalized information-theoretic discrepancy (GID) was employed as a new metric for quantifying the distance between the measured and synthetic data. By using special features of the GID, the objective function for polychromatic reconstruction which contains a double integral over the wavelength and the trajectory of incident x-rays was simplified to a paraboloidal form without using the monochromatic approximation. More specifically, the original GID was replaced with a surrogate function with two auxiliary, energy-dependent variables. Subsequently, the alternating minimization technique was applied to solve the double minimization problem. Based on the optimization transfer principle, the objective function was further simplified to the paraboloidal equation, which leads to a closed-form update formula. Numerical experiments on the beam-hardening correction and material-selective reconstruction were conducted to compare and assess the performance of conventional methods and the proposed algorithms. RESULTS: The authors found that the GID determines the distance between its two arguments in a flexible manner. In this study, three groups of GIDs with distinct data representations were considered. The authors demonstrated that one type of GIDs that comprises "raw" data can be viewed as an extension of existing statistical reconstructions; under a particular condition, the GID is equivalent to the Poisson log-likelihood function. The newly proposed GIDs of the other two categories consist of log-transformed measurements, which have the advantage of imposing linearized penalties over multiple discrepancies. For all proposed variants of the GID, the aforementioned strategy was used to obtain a closed-form update equation. Even though it is based on the exact polychromatic model, the derived algorithm bears a structural resemblance to conventional methods based on the monochromatic approximation. The authors named the proposed approach as information-theoretic discrepancy based iterative reconstructions (IDIR). In numerical experiments, IDIR with raw data converged faster than previously known statistical reconstruction methods. IDIR with log-transformed data exhibited superior reconstruction quality and faster convergence speed compared with conventional methods and their variants. CONCLUSIONS: The authors' new framework for tomographic reconstruction allows iterative inversion of the polychromatic data model. The primary departure from the traditional iterative reconstruction was the employment of the GID as a new metric for quantifying the inconsistency between the measured and synthetic data. The proposed methods outperformed not only conventional methods based on the monochromatic approximation but also those based on the polychromatic model. The authors have observed that the GID is a very flexible means to design an objective function for iterative reconstructions. Hence, the authors expect that the proposed IDIR framework will also be applicable to other challenging tasks.

A unified statistical framework for material decomposition using multienergy photon counting x-ray detectors.

PURPOSE: Material decomposition using multienergy photon counting x-ray detectors (PCXD) has been an active research area over the past few years. Even with some success, the problem of optimal energy selection and three material decomposition including malignant tissue is still on going research topic, and more systematic studies are required. This paper aims to address this in a unified statistical framework in a mammographic environment. METHODS: A unified statistical framework for energy level optimization and decomposition of three materials is proposed. In particular, an energy level optimization algorithm is derived using the theory of the minimum variance unbiased estimator, and an iterative algorithm is proposed for material composition as well as system parameter estimation under the unified statistical estimation framework. To verify the performance of the proposed algorithm, the authors performed simulation studies as well as real experiments using physical breast phantom and ex vivo breast specimen. Quantitative comparisons using various performance measures were conducted, and qualitative performance evaluations for ex vivo breast specimen were also performed by comparing the ground-truth malignant tissue areas identified by radiologists. RESULTS: Both simulation and real experiments confirmed that the optimized energy bins by the proposed method allow better material decomposition quality. Moreover, for the specimen thickness estimation errors up to 2 mm, the proposed method provides good reconstruction results in both simulation and real ex vivo breast phantom experiments compared to existing methods. CONCLUSIONS: The proposed statistical framework of PCXD has been successfully applied for the energy optimization and decomposition of three material in a mammographic environment. Experimental results using the physical breast phantom and ex vivo specimen support the practicality of the proposed algorithm.

Directing the deposition of ferromagnetic cobalt onto Pt-tipped CdSe@CdS nanorods: synthetic and mechanistic insights.

A methodology providing access to dumbbell-tipped, metal-semiconductor and metal oxide-semiconductor heterostructured nanorods has been developed. The synthesis and characterization of CdSe@CdS nanorods incorporating ferromagnetic cobalt nanoinclusions at both nanorod termini (i.e., dumbbell morphology) are presented. The key step in the synthesis of these heterostructured nanorods was the decoration of CdSe@CdS nanorods with platinum nanoparticle tips, which promoted the deposition of metallic CoNPs onto Pt-tipped CdSe@CdS nanorods. Cobalt nanoparticle tips were then selectively oxidized to afford CdSe@CdS nanorods with cobalt oxide domains at both termini. In the case of longer cobalt-tipped nanorods, heterostructured nanorods were observed to self-organize into complex dipolar assemblies, which formed as a consequence of magnetic associations of terminal CoNP tips. Colloidal polymerization of these cobalt-tipped nanorods afforded fused nanorod assemblies from the oxidation of cobalt nanoparticle tips at the ends of nanorods via the nanoscale Kirkendall effect. Wurtzite CdS nanorods survived both the deposition of metallic CoNP tips and conversion into cobalt oxide phases, as confirmed by both XRD and HRTEM analysis. A series of CdSe@CdS nanorods of four different lengths ranging from 40 to 174 nm and comparable diameters (6-7 nm) were prepared and modified with both cobalt and cobalt oxide tips. The total synthesis of these heterostructured nanorods required five steps from commercially available reagents. Key synthetic considerations are discussed, with particular emphasis on reporting isolated yields of all intermediates and products from scale up of intermediate precursors.