A Deep Learning Reconstruction Framework for Differential Phase-Contrast Computed Tomography with Incomplete Data

Differential phase-contrast computed tomography (DPC-CT) is a powerful analysis tool for soft-tissue and low-atomic-number samples. Limited by the implementation conditions, DPC-CT with incomplete projections happens quite often. Conventional reconstruction algorithms are not easy to deal with incomplete data. They are usually involved with complicated parameter selection operations, also sensitive to noise and time-consuming. In this paper, we reported a new deep learning reconstruction framework for incomplete data DPC-CT. It is the tight coupling of the deep learning neural network and DPC-CT reconstruction algorithm in the phase-contrast projection sinogram domain. The estimated result is the complete phase-contrast projection sinogram not the artifacts caused by the incomplete data. After training, this framework is determined and can reconstruct the final DPC-CT images for a given incomplete phase-contrast projection sinogram. Taking the sparse-view DPC-CT as an example, this framework has been validated and demonstrated with synthetic and experimental data sets. Embedded with DPC-CT reconstruction, this framework naturally encapsulates the physical imaging model of DPC-CT systems and is easy to be extended to deal with other challengs. This work is helpful to push the application of the state-of-the-art deep learning theory in the field of DPC-CT

Voltage-dependent Block of the Cystic Fibrosis Transmembrane Conductance Regulator Cl- Channel by Two Closely Related Arylaminobenzoates

The gene defective in cystic fibrosis encodes a Cl- channel, the cystic fibrosis transmembrane conductance regulator (CFTR). CFTR is blocked by diphenylamine-2-carboxylate (DPC) when applied extracellularly at millimolar concentrations. We studied the block of CFTR expressed in Xenopus oocytes by DPC or by a closely related molecule, flufenamic acid (FFA). Block of whole-cell CFTR currents by bath-applied DPC or by FFA, both at 200 µM, requires several minutes to reach full effect. Blockade is voltage dependent, suggesting open-channel block: currents at positive potentials are not affected but currents at negative potentials are reduced. The binding site for both drugs senses ~40% of the electric field across the membrane, measured from the inside. In single-channel recordings from excised patches without blockers, the conductance was 8.0 ± 0.4 pS in symmetric 150 mM Cl^-. A subconductance state, measuring ~60% of the main conductance, was often observed. Bursts to the full open state lasting up to tens of seconds were uninterrupted at depolarizing membrane voltages. At hyperpolarizing voltages, bursts were interrupted by brief closures. Either DPC or FFA (50 µM) applied to the cytoplasmic or extracellular face of the channel led to an increase in flicker at V_m =-100 mV and not at V_m = +100 mV, in agreement with whole-cell experiments. DPC induced a higher frequency of flickers from the cytoplasmic side than the extracellular side. FFA produced longer closures than DPC; the FFA closed time was roughly equal (~ 1.2 ms) at -100 mV with application from either side. In cell-attached patch recordings with DPC or FFA applied to the bath, there was flickery block at V_m = -100 mV, confirming that the drugs permeate through the membrane to reach the binding site. The data are consistent with the presence of a single binding site for both drugs, reached from either end of the channel. Open-channel block by DPC or FFA may offer tools for use with site-directed mutagenesis to describe the permeation pathway

Influence of GABA and GABA-producing Lactobacillus brevis DPC 6108 on the development of diabetes in a streptozotocin rat model

peer-reviewedThe aim of this study was to investigate if dietary administration of γ-aminobutyric acid (GABA)-producing Lactobacillus brevis DPC 6108 and pure GABA exert protective effects against the development of diabetes in streptozotocin (STZ)-induced diabetic Sprague Dawley rats. In a first experiment, healthy rats were divided in 3 groups (n=10/group) receiving placebo, 2.6 mg/kg body weight (bw) pure GABA or L. brevis DPC 6108 (~109microorganisms). In a second experiment, rats (n=15/group) were randomised to five groups and four of these received an injection of STZ to induce type 1 diabetes. Diabetic and non-diabetic controls received placebo [4% (w/v) yeast extract in dH2O], while the other three diabetic groups received one of the following dietary supplements: 2.6 mg/kg bw GABA (low GABA), 200 mg/kg bw GABA (high GABA) or ~109 L. brevis DPC 6108. L. brevis DPC 6108 supplementation was associated with increased serum insulin levels (P0.05), compared with non-diabetic controls while all other diabetic groups displayed reduced diversity (P<0.05). L. brevis DPC 6108 attenuated hyperglycaemia induced by diabetes but additional studies are needed to understand the mechanisms involved in this reduction.The authors and their work were supported by the APC Microbiome Institute. The APC Microbiome Institute is funded by Science Foundation Ireland (SFI). This publication has emanated from research supported by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2273

A Comparison of Time-Sharing, DPC, and Beamforming for MIMO Broadcast Channels With Many Users

In this letter, we derive the scaling laws of the sum rate for fading multiple-input multiple-output Gaussian broadcast channels using time sharing to the strongest user, dirty-paper coding (DPC), and beamforming, when the number of users (receivers) n is large. Throughout the letter, we assume a fix average transmit power and consider a block-fading Rayleigh channel. First, we show that for a system with M transmit antennas and users equipped with N antennas, the sum rate scales like M log logn N for DPC, and beamforming when M is fixed and for any N (either growing to infinity or not). On the other hand, when both M and N are fixed, the sum rate of time sharing to the strongest user scales like min(M,N)log log n. Therefore, the asymptotic gain of DPC over time sharing for the sum rate is (M/min(M,N)) when M and N are fixed. It is also shown that if M grows as logn, the sum rate of DPC and beamforming will grow linearly in M, but with different constant multiplicative factors. In this region, the sum-rate capacity of time-sharing scales like N log log n

Scaling laws of sum rate using time-sharing, DPC, and beamforming for MIMO broadcast channels

We derive the scaling laws of the sum rate throughput for MIMO Gaussian broadcast channels using time-sharing to the strongest user, dirty paper coding (DPC), and beamforming when the number of users (receivers) n is large. Assuming a fixed total average transmit power, we show that for a system with M transmit antennas and users equipped with N antennas, the sum rate scales like M log log nN for DPC and beamforming when M is fixed and for any N (either growing to infinity or not). On the other hand, when both M and N are fixed, the sum rate of time-sharing to the strongest user scales like min(M,N) log log n. It is also shown that if M grows as log n, the sum rate of DPC and beamforming will grow linearly in M, but with different constant multiplicative factors. In this region, the sum rate capacity of time-sharing scales like N log log n