Dual-polarization RF channelizer based on microcombs.

We report a dual-polarization radio frequency (RF) channelizer based on microcombs. Two high-Q micro-ring resonators (MRRs) with slightly different free spectral ranges (FSRs) are used: one MRR is pumped to yield soliton crystal microcombs ("active"), and the other MRR is used as a "passive" periodic optical filter supporting dual-polarization operation to slice the RF spectrum. With the tailored mismatch between the FSRs of the active and passive MRRs, wideband RF spectra can be channelized into multiple segments featuring digital-compatible bandwidths via the Vernier effect. Due to the use of dual-polarization states, the number of channelized spectral segments, and thus the RF instantaneous bandwidth (with a certain spectral resolution), can be doubled. In our experiments, we used 20 microcomb lines with ∼ 49 GHz FSR to achieve 20 channels for each polarization, with high RF spectra slicing resolutions at 144 MHz (TE) and 163 MHz (TM), respectively; achieving an instantaneous RF operation bandwidth of 3.1 GHz (TE) and 2.2 GHz (TM). Our approach paves the path towards monolithically integrated photonic RF receivers (the key components - active and passive MRRs are all fabricated on the same platform) with reduced complexity, size, and unprecedented performance, which is important for wide RF applications with digital-compatible signal detection.

Brønsted Acid-Catalyzed Synthesis of 1,2,5-Trisubstituted Imidazoles via a Multicomponent Reaction of Vinyl Azides with Aromatic Aldehydes and Aromatic Amines.

A facile and efficient approach to the synthesis of 1,2,5-trisubstituted imidazoles is developed via a multicomponent reaction under metal-free catalysis. Under Brønsted acid catalysis, the desired products can be obtained from readily available vinyl azides, aromatic aldehydes, and aromatic amines without generating any toxic waste. The convenient operations and high functional group compatibility indicate that this approach offers an attractive alternative method for the synthesis of imidazole derivatives.

Multiple isotopic tracing for sulfate and base cation sources of precipitation in Hangzhou city, Southeast China: Insights for rainwater acidification mechanism.

Acid deposition has been regarded as a serious factor in the deteriorative water environment and ecosystems. Despite the powerful acid emission control measures have been implemented by the Chinese government, many areas (especially Southeast China) are still suffering from acid deposition. The chemical and isotopic (δ34S and 87Sr/86Sr) compositions of rainwater in Hangzhou, a typical megacity in Southeast China with serious acid rain problem, for one year were studied with the aim to better constrain potential sources and explore the causes of rainwater acidification. Most rainwater samples were acidic, with a VWM pH value of 4.65. SO42- was the dominant anion and the main acid ion in rainwater. Sulfur isotope and the quantity equilibrium model revealed that sea salt, crustal, biogenic, and anthropogenic sulfur represented 2.3%, 0.1%, 16.7%, and 80.8% of the SO42- source in rainwater, respectively. The back trajectory and strontium isotopes indicated that the base cations (BCs) in rainwater originated mainly from anthropogenic sources. The relatively low neutralizing capacity caused by limited BCs input and emission control measures undermines some efforts to reduce rainwater acidity. This case study demonstrated that a valuable tool to probe the source of acid rain and unravel the mechanism of rainwater acidification can be provided by multiple lines of evidence, including rainwater chemical compositions, stable sulfur isotopes, and stable strontium isotopes.

X-ray cone-beam computed tomography geometric artefact reduction based on a data-driven strategy.

Cone-beam computed tomography (CBCT) enables three-dimensional imaging of the internal structure of objects in a non-invasive way with high accuracy. Practical misalignment of the CBCT system causes geometric artefacts in reconstructed images, which seriously degrades image quality in ways such as detail loss and decreased spatial resolution. This leads to inaccurate distinction of defects in detection, especially in precise industrial fields like aerospace and instrument manufacturing. This paper presents a method to reduce the geometric artefacts based on a data-driven strategy, which is an end-to-end modified fully convolutional neural network (M-FCNN). The designed M-FCCN contains five convolution layers and five deconvolution layers for feature extraction and output image rebuilding, respectively. In addition, the pooling layer is not used in the designed M-FCNN, considering the preservation of details in the reconstructed image. In this M-FCCN, artefact images with different features have been trained separately. After training, the M-FCNN can be applied to directly reduce geometric artefacts in the reconstructed image. The designed M-FCNN has been demonstrated with different types of synthetic data and has achieved accurate results. It is also validated with practical data, including carbon composite and medical oral phantoms with comparable quality to phantom-based methods, proving that it is an effective way to reduce geometric artefacts in the image domain by means of a data-driven strategy.

Image Decomposition Algorithm for Dual-Energy Computed Tomography via Fully Convolutional Network.

BACKGROUND: Dual-energy computed tomography (DECT) has been widely used due to improved substances identification from additional spectral information. The quality of material-specific image produced by DECT attaches great importance to the elaborated design of the basis material decomposition method. OBJECTIVE: The aim of this work is to develop and validate a data-driven algorithm for the image-based decomposition problem. METHODS: A deep neural net, consisting of a fully convolutional net (FCN) and a fully connected net, is proposed to solve the material decomposition problem. The former net extracts the feature representation of input reconstructed images, and the latter net calculates the decomposed basic material coefficients from the joint feature vector. The whole model was trained and tested using a modified clinical dataset. RESULTS: The proposed FCN delivers image with about 60% smaller bias and 70% lower standard deviation than the competing algorithms, suggesting its better material separation capability. Moreover, FCN still yields excellent performance in case of photon noise. CONCLUSIONS: Our deep cascaded network features high decomposition accuracies and noise robust property. The experimental results have shown the strong function fitting ability of the deep neural network. Deep learning paradigm could be a promising way to solve the nonlinear problem in DECT.

Projection decomposition algorithm for dual-energy computed tomography via deep neural network.

BACKGROUND: Dual-energy computed tomography (DECT) has been widely used to improve identification of substances from different spectral information. Decomposition of the mixed test samples into two materials relies on a well-calibrated material decomposition function. OBJECTIVE: This work aims to establish and validate a data-driven algorithm for estimation of the decomposition function. METHODS: A deep neural network (DNN) consisting of two sub-nets is proposed to solve the projection decomposition problem. The compressing sub-net, substantially a stack auto-encoder (SAE), learns a compact representation of energy spectrum. The decomposing sub-net with a two-layer structure fits the nonlinear transform between energy projection and basic material thickness. RESULTS: The proposed DNN not only delivers image with lower standard deviation and higher quality in both simulated and real data, and also yields the best performance in cases mixed with photon noise. Moreover, DNN costs only 0.4 s to generate a decomposition solution of 360 × 512 size scale, which is about 200 times faster than the competing algorithms. CONCLUSIONS: The DNN model is applicable to the decomposition tasks with different dual energies. Experimental results demonstrated the strong function fitting ability of DNN. Thus, the Deep learning paradigm provides a promising approach to solve the nonlinear problem in DECT.

Grb2-associated binder 2 silencing impairs growth and migration of H1975 cells via modulation of PI3K-Akt signaling.

Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related death and often has a poor prognosis. Investigation of NSCLC cancer cell migration, invasion and development of strategies to block this process is essential to improve the disease prognosis. In this study, we tested our hypothesis that Grb2-associated binder 2 (Gab2) regulate NSCLC cancer cell H1975 malignant biological behaviors, and silencing Gab2 reduced H1975 cellular colony forming ability, migration and invasion. Moreover, silenced cells present defects in phosphatidylinositol 3-kinase (PI3K)-serine/threonine kinase (Akt) signaling, and reduced expression/activity of matrix metallopeptidase (MMP)-2/9. Furthermore, in Gab2 siRNA-transfected cells, we detected a decrease in signal transducer and activator of transcription 3 (STAT3) phosphorylation and nuclear translocation. In vivo, Gab2 siRNA cells inoculated subcutaneously in nude mice demonstrated decreased tumor growth and PI3K-Akt signaling inhibition. These results indicate that Gab2 is a key factor in H1975 tumor migration, invasion, suggesting that Gab2 can be a novel therapeutic target in NSCLC.