7TMRmine: a Web server for hierarchical mining of 7TMR proteins

Background: Seven-transmembrane region-containing receptors (7TMRs) play central roles in eukaryotic signal transduction. Due to their biomedical importance, thorough mining of 7TMRs from diverse genomes has been an active target of bioinformatics and pharmacogenomics research. The need for new and accurate 7TMR/GPCR prediction tools is paramount with the accelerated rate of acquisition of diverse sequence information. Currently available and often used protein classification methods (e.g., profile hidden Markov Models) are highly accurate for identifying their membership information among already known 7TMR subfamilies. However, these alignment-based methods are less effective for identifying remote similarities, e.g., identifying proteins from highly divergent or possibly new 7TMR families. In this regard, more sensitive (e.g., alignment-free) methods are needed to complement the existing protein classification methods. A better strategy would be to combine different classifiers, from more specific to more sensitive methods, to identify a broader spectrum of 7TMR protein candidates. Description: We developed a Web server, 7TMRmine, by integrating alignment-free and alignment-based classifiers specifically trained to identify candidate 7TMR proteins as well as transmembrane (TM) prediction methods. This new tool enables researchers to easily assess the distribution of GPCR functionality in diverse genomes or individual newly-discovered proteins. 7TMRmine is easily customized and facilitates exploratory analysis of diverse genomes. Users can integrate various alignment-based, alignment-free, and TM-prediction methods in any combination and in any hierarchical order. Sixteen classifiers (including two TM-prediction methods) are available on the 7TMRmine Web server. Not only can the 7TMRmine tool be used for 7TMR mining, but also for general TM-protein analysis. Users can submit protein sequences for analysis, or explore pre-analyzed results for multiple genomes. The server currently includes prediction results and the summary statistics for 68 genomes. Conclusion: 7TMRmine facilitates the discovery of 7TMR proteins. By combining prediction results from different classifiers in a multi-level filtering process, prioritized sets of 7TMR candidates can be obtained for further investigation. 7TMRmine can be also used as a general TM-protein classifier. Comparisons of TM and 7TMR protein distributions among 68 genomes revealed interesting differences in evolution of these protein families among major eukaryotic phyla

Cavity-enhanced and spatial-multimode spin-wave-photon quantum interface

Practical realizations of quantum repeaters require quantum memory simultaneously providing high retrieval efficiency, long lifetime and multimode storages. So far, the combination of high retrieval efficiency and spatially multiplexed storages into a single memory remains challenging. Here, we set up a ring cavity that supports an array including 6 TEM00 modes and then demonstrated cavity enhanced and spatially multiplexed spin wave photon quantum interface (QI). The cavity arrangement is according to Fermat' optical theorem, which enables the six modes to experience the same optical length per round trip. Each mode includesn horizontal and vertical polarizations. Via DLCZ process in a cold atomic ensemble, we create non classically correlated pairs of spin waves and Stokes photons in the 12 modes. The retrieved fields from the multiplexed SWs are enhanced by the cavity and the average intrinsic retrieval efficiency reaches 70% at zero delay. The storage time for the case that cross-correlation function of the multiplexed QI is beyond 2 reaches 0.6ms

Element Replacement Approach by Reaction with Lewis Acidic Molten Salts to Synthesize Nanolaminated MAX Phases and MXenes

Nanolaminated materials are important because of their exceptional properties and wide range of applications. Here, we demonstrate a general approach to synthesize a series of Zn-based MAX phases and Cl-terminated MXenes originating from the replacement reaction between the MAX phase and the late transition metal halides. The approach is a top-down route that enables the late transitional element atom (Zn in the present case) to occupy the A site in the pre-existing MAX phase structure. Using this replacement reaction between Zn element from molten ZnCl2 and Al element in MAX phase precursors (Ti3AlC2, Ti2AlC, Ti2AlN, and V2AlC), novel MAX phases Ti3ZnC2, Ti2ZnC, Ti2ZnN, and V2ZnC were synthesized. When employing excess ZnCl2, Cl terminated MXenes (such as Ti3C2Cl2 and Ti2CCl2) were derived by a subsequent exfoliation of Ti3ZnC2 and Ti2ZnC due to the strong Lewis acidity of molten ZnCl2. These results indicate that A-site element replacement in traditional MAX phases by late transition metal halides opens the door to explore MAX phases that are not thermodynamically stable at high temperature and would be difficult to synthesize through the commonly employed powder metallurgy approach. In addition, this is the first time that exclusively Cl-terminated MXenes were obtained, and the etching effect of Lewis acid in molten salts provides a green and viable route to prepare MXenes through an HF-free chemical approach.Comment: Title changed; experimental section and discussion revise

MTANS:Multi-Scale Mean Teacher Combined Adversarial Network with Shape-Aware Embedding for Semi-Supervised Brain Lesion Segmentation

The annotation of brain lesion images is a key step in clinical diagnosis and treatment of a wide spectrum of brain diseases. In recent years, segmentation methods based on deep learning have gained unprecedented popularity, leveraging a large amount of data with high-quality voxel-level annotations. However, due to the limited time clinicians can provide for the cumbersome task of manual image segmentation, semi-supervised medical image segmentation methods present an alternative solution as they require only a few labeled samples for training. In this paper, we propose a novel semi-supervised segmentation framework that combines improved mean teacher and adversarial network. Specifically, our framework consists of (i) a student model and a teacher model for segmenting the target and generating the signed distance maps of object surfaces, and (ii) a discriminator network for extracting hierarchical features and distinguishing the signed distance maps of labeled and unlabeled data. Besides, based on two different adversarial learning processes, a multi-scale feature consistency loss derived from the student and teacher models is proposed, and a shape-aware embedding scheme is integrated into our framework. We evaluated the proposed method on the public brain lesion datasets from ISBI 2015, ISLES 2015, and BRATS 2018 for the multiple sclerosis lesion, ischemic stroke lesion, and brain tumor segmentation respectively. Experiments demonstrate that our method can effectively leverage unlabeled data while outperforming the supervised baseline and other state-of-the-art semi-supervised methods trained with the same labeled data. The proposed framework is suitable for joint training of limited labeled data and additional unlabeled data, which is expected to reduce the effort of obtaining annotated images

Assessment of the three-dimensional flow field in the reactor pressure vessel in Hualong One nuclear power plants

This study uses computational fluid dynamics (CFD) to investigate the three-dimensional flow field under normal operating conditions in the reactor pressure vessel (RPV) in the Hualong One nuclear power plants (NPPs). With a particular focus on the flowrate distribution at the core inlet, the numerical framework is validated against the integral hydraulic experiment in a 1:4-scaled RPV of CNP1000, the prototype of the Hualong One reactor. The simulation results of the normalized flowrate at the core inlet agree reasonably well with the measured data. Based on the experimental data, several methods of calibrating the CFD turbulence model coefficients are suggested by introducing the concepts of data assimilation and machine learning. The flow field in a realistic RPV for Hualong One is predicted using the validated numerical framework, showing that the flowrate distribution at the core inlet is nearly homogeneous and that the turbulent intensity is acceptably low for each fuel assembly. It can provide essential information for the reactor core thermal–hydraulic design and the fuel assembly mechanical assessment

Nanowrinkled Carbon Aerogels Embedded with FeN x Sites as Effective Oxygen Electrodes for Rechargeable Zinc-Air Battery.

Rational design of single-metal atom sites in carbon substrates by a flexible strategy is highly desired for the preparation of high-performance catalysts for metal-air batteries. In this study, biomass hydrogel reactors are utilized as structural templates to prepare carbon aerogels embedded with single iron atoms by controlled pyrolysis. The tortuous and interlaced hydrogel chains lead to the formation of abundant nanowrinkles in the porous carbon aerogels, and single iron atoms are dispersed and stabilized within the defective carbon skeletons. X-ray absorption spectroscopy measurements indicate that the iron centers are mostly involved in the coordination structure of FeN4, with a minor fraction (ca. 1/5) in the form of FeN3C. First-principles calculations show that the FeN x sites in the Stone-Wales configurations induced by the nanowrinkles of the hierarchically porous carbon aerogels show a much lower free energy than the normal counterparts. The resulting iron and nitrogen-codoped carbon aerogels exhibit excellent and reversible oxygen electrocatalytic activity, and can be used as bifunctional cathode catalysts in rechargeable Zn-air batteries, with a performance even better than that based on commercial Pt/C and RuO2 catalysts. Results from this study highlight the significance of structural distortions of the metal sites in carbon matrices in the design and engineering of highly active single-atom catalysts