Rice LIKE EARLY STARVATION1 cooperates with FLOURY ENDOSPERM6 to modulate starch biosynthesis and endosperm development.

In cereal grains, starch is synthesized by the concerted actions of multiple enzymes on the surface of starch granules within the amyloplast. However, little is known about how starch-synthesizing enzymes access starch granules, especially for amylopectin biosynthesis. Here, we show that the rice (Oryza sativa) floury endosperm9 (flo9) mutant is defective in amylopectin biosynthesis, leading to grains exhibiting a floury endosperm with a hollow core. Molecular cloning revealed that FLO9 encodes a plant-specific protein homologous to Arabidopsis (Arabidopsis thaliana) LIKE EARLY STARVATION1 (LESV). Unlike Arabidopsis LESV, which is involved in starch metabolism in leaves, OsLESV is required for starch granule initiation in the endosperm. OsLESV can directly bind to starch by its C-terminal tryptophan (Trp)-rich region. Cellular and biochemical evidence suggests that OsLESV interacts with the starch-binding protein FLO6, and loss-of-function mutations of either gene impair ISOAMYLASE1 (ISA1) targeting to starch granules. Genetically, OsLESV acts synergistically with FLO6 to regulate starch biosynthesis and endosperm development. Together, our results identify OsLESV-FLO6 as a non-enzymatic molecular module responsible for ISA1 localization on starch granules, and present a target gene for use in biotechnology to control starch content and composition in rice endosperm.

Autofocus using adaptive prediction approximation combined search for the fluorescence microscope in second-generation DNA sequencing system.

Autofocus is an important technique for high-speed image acquisition in the second-generation DNA sequencing system, and this paper studies the passive focus algorithm for the system, which consists of two parts: focus measurement (FM) and focus search (FS). Based on the properties of DNA chips' images, we choose the normalized variance as the FM algorithm and develop a new robust FS named adaptive prediction approximation combined search (APACS). APACS utilizes golden section search (GSS) to approximate the focus position and engages the curve-fitting search (CFS) to predict the position simultaneously in every step of GSS. When the difference between consecutive predictions meets the set precision, the search finishes. Otherwise, it ends as GSS. In APACS, we also propose an estimation method, named the combination of centroid estimation and overdetermined equations estimation by least squares solution, to calculate the initial vector for the nonlinear equations in APACS prediction, which reduces the iterations and accelerates the search. The simulation and measured results demonstrate that APACS not only maintains the stability but also reduces the focus time compared with GSS and CFS, which indicates APACS is a robust and fast FS for the fluorescence microscope in a sequencing system.

Scatter Correction Based on GPU-Accelerated Full Monte Carlo Simulation for Brain PET/MRI.

Accurate scatter correction is essential for qualitative and quantitative PET imaging. Until now, scatter correction based on Monte Carlo simulation (MCS) has been recognized as the most accurate method of scatter correction for PET. However, the major disadvantage of MCS is its long computational time, which makes it unfeasible for clinical usage. Meanwhile, single scatter simulation (SSS) is the most widely used method for scatter correction. Nevertheless, SSS has the disadvantage of limited robustness for dynamic measurements and for the measurement of large objects. In this work, a newly developed implementation of MCS using graphics processing unit (GPU) acceleration is employed, allowing full MCS-based scatter correction in clinical 3D brain PET imaging. Starting from the generation of annihilation photons to their detection in the simulated PET scanner, all relevant physical interactions and transport phenomena of the photons were simulated on GPUs. This resulted in an expected distribution of scattered events, which was subsequently used to correct the measured emission data. The accuracy of the approach was validated with simulations using GATE (Geant4 Application for Tomography Emission), and its performance was compared to SSS. The comparison of the computation time between a GPU and a single-threaded CPU showed an acceleration factor of 776 for a voxelized brain phantom study. The speedup of the MCS implemented on the GPU represents a major step toward the application of the more accurate MCS-based scatter correction for PET imaging in clinical routine.

Resolution modeling in projection space using a factorized multi-block detector response function for PET image reconstruction.

Positron emission tomography (PET) images usually suffer from limited resolution and statistical uncertainties. However, a technique known as resolution modeling (RM) can be used to improve image quality by accurately modeling the system's detection process within the iterative reconstruction. In this study, we present an accurate RM method in projection space based on a simulated multi-block detector response function (DRF) and evaluate it on the Siemens hybrid MR-BrainPET system. The DRF is obtained using GATE simulations that consider nearly all the possible annihilation photons from the field-of-view (FOV). Intrinsically, the multi-block DRF allows the block crosstalk to be modeled. The RM blurring kernel is further generated by factorizing the blurring matrix of one line-of-response (LOR) into two independent detector responses, which can then be addressed with the DRF. Such a kernel is shift-variant in 4D projection space without any distance or angle compression, and is integrated into the image reconstruction for the BrainPET insert with single instruction multiple data (SIMD) and multi-thread support. Evaluation of simulations and measured data demonstrate that the reconstruction with RM yields significantly improved resolutions and reduced mean squared error (MSE) values at different locations of the FOV, compared with reconstruction without RM. Furthermore, the shift-variant RM kernel models the varying blurring intensity for different LORs due to the depth-of-interaction (DOI) dependencies, thus avoiding severe edge artifacts in the images. Additionally, compared to RM in single-block mode, the multi-block mode shows significantly improved resolution and edge recovery at locations beyond 10 cm from the center of BrainPET insert in the transverse plane. However, the differences have been observed to be low for patient data between single-block and multi-block mode RM, due to the brain size and location as well as the geometry of the BrainPET insert. In conclusion, the RM method proposed in this study can yield better reconstructed images in terms of resolution and MSE value, compared to conventional reconstruction without RM.

Delivery of PUMA Apoptosis Gene Using Polyethyleneimine-SMCC-TAT/DNA Nanoparticles: Biophysical Characterization and In Vitro Transfection Into Malignant Melanoma Cells.

A synthesized PEI-based gene delivery system, wherein PEI was crosslinked with sulfosuccinimidyl-4-(N-maleimidomethyl) cyclohexane-1-carboxylate (Sulfo-SMCC) conjugating trans-activating transcriptional activator (TAT), yielding PEI-SMCC-TAT (PST), a novel non-viral vector for apoptosis-related gene PUMA (p53 up regulated modulator of apoptosis), was designed and evaluated. Sulfo-SMCC is a commonly used heterobifunctional crosslinker and is soluble in water, making the crosslinking easier without organic reagent like DMSO or chloroform. The PST/pDNA nanoparticles were 171.9 nm at the optimal N/P ratio (50:1). DNA complexes of all the PST conjugation had much lower toxicity and exhibited enhancement in transfection efficiency in comparison with single PEI vector. The results also showed that the transfection efficiency of PST/pEGFP nanoparticles into malignant melanoma A375 cell increased, and PST carrying PUMA gene induced the apoptosis of A375 cells. It was suggested that PST could be a promising melanoma tumor-targeting nanovector, and have a good potential in clinical application.