The oyster genome reveals stress adaptation and complexity of shell formation

The Pacific oyster Crassostrea gigas belongs to one of the most species-rich but genomically poorly explored phyla, the Mollusca. Here we report the sequencing and assembly of the oyster genome using short reads and a fosmid-pooling strategy, along with transcriptomes of development and stress response and the proteome of the shell. The oyster genome is highly polymorphic and rich in repetitive sequences, with some transposable elements still actively shaping variation. Transcriptome studies reveal an extensive set of genes responding to environmental stress. The expansion of genes coding for heat shock protein 70 and inhibitors of apoptosis is probably central to the oyster's adaptation to sessile life in the highly stressful intertidal zone. Our analyses also show that shell formation in molluscs is more complex than currently understood and involves extensive participation of cells and their exosomes. The oyster genome sequence fills a void in our understanding of the Lophotrochozoa. © 2012 Macmillan Publishers Limited. All rights reserved

Joint Inversion of Evaporation Duct Based on Radar Sea Clutter and Target Echo Using Deep Learning

Tropospheric duct is an anomalous atmospheric phenomenon over the sea surface that seriously affects the normal operation and performance evaluation of electromagnetic communication equipment at sea. Therefore, achieving precise sensing of tropospheric duct is of profound significance for the propagation of electromagnetic signals. The approach of inverting atmospheric refractivity from easily measurable radar sea clutter is also known as the refractivity from clutter (RFC) technique. However, inversion precision of the conventional RFC technique is low in the low-altitude evaporation duct environment. Due to the weak attenuation of the over-the-horizon target signal as it passes through the tropospheric duct, its strength is much stronger than that of sea clutter. Therefore, this study proposes a new method for the joint inversion of evaporation duct height (EDH) based on sea clutter and target echo by combining deep learning. By testing the inversion performance and noise immunity of the new joint inversion method, the experimental results show that the mean error RMSE and MAE of the new method proposed in this paper are reduced by 41.2% and 40.3%, respectively, compared with the conventional method in the EDH range from 0 to 40 m. In particular, the RMSE and MAE in the EDH range from 0 to 16.7 m are reduced by 54.2% and 56.4%, respectively, compared with the conventional method. It shows that the target signal is more sensitive to the lower evaporation duct, which obviously enhances the inversion precision of the lower evaporation duct and has effectively improved the weak practicality of the conventional RFC technique

Discovery of (S)-6-methoxy-chroman-3-carboxylic acid (4-pyridin-4-yl-phenyl)-amide as potent and isoform selective ROCK2 inhibitors

[Display omitted] ROCK1 and ROCK2 are highly homologous isoforms. Accumulated studies indicate that they have distinct different functions, and the development of isoform selective ROCK inhibitors will pave new roads for the treatment of various diseases. In this work, a series of amide-chroman derivatives were synthesized and biologically evaluated in order to develop potent and isoform selective ROCK2 inhibitors. Remarkably, (S)-6-methoxy-chroman-3-carboxylic acid (4-pyridin-4-yl-phenyl)-amide ((S)-7c) possessed ROCK2 inhibitory activity with an IC50 value of 3 nM and 22.7-fold isoform selectivity (vs. ROCK1). Molecular docking indicated that hydrophobic interactions were the key element for the high potency and isoform selectivity of (S)-7c. The binding free energies predicted by MM/GBSA were in good agreement with the experimental bioactivities, and the analysis of individual energy terms suggested that residue Lys105 in ROCK1 or Lys121 in ROCK2 was the key residue for the isoform selectivity of (S)-7c

Computer-aided discovery of phenylpyrazole based amides as potent S6K1 inhibitors

Ribosomal protein S6 kinase beta-1 (S6K1) is an attractive therapeutic target. In this study, computational analysis of five thiophene urea-based S6K1 inhibitors was performed. Molecular docking showed that the five compounds formed hydrogen bonds with residues Glu173 and Leu175 of S6K1 and hydrophobic interactions with residues Val105, Leu97 and Met225, and these interactions were key elements for the inhibitory potency of the compounds. Binding free energy (Δ ) decomposition analysis showed that Leu97, Glu173, Val 105, Leu175, Leu97 and Met225 contribute the most to Δ . Based on the computer results, phenylpyrazole based amides ( ) were designed and synthesized. Biological evaluation revealed that exhibited 15.9 nM S6K1 inhibition, medium microsomal stability and desirable bioavailability

Multiscale Decomposition Prediction of Propagation Loss for EM Waves in Marine Evaporation Duct Using Deep Learning

A tropospheric duct (TD) is an anomalous atmospheric refraction structure in marine environments that seriously interferes with the propagation path and range of electromagnetic (EM) waves, resulting in serious influence on the normal operation of radar. Since the propagation loss (PL) can reflect the propagation characteristics of EM waves inside the duct layer, it is important to obtain an accurate cognition of the PL of EM waves in marine TDs. However, the PL is strongly non−linear with propagation range due to the trapped propagation effect inside duct layer, which makes accurate prediction of PL more difficult. To resolve this problem, a novel multiscale decomposition prediction method (VMD−PSO−LSTM) based on the long short−term memory (LSTM) network, variational mode decomposition (VMD) method and particle swarm optimization (PSO) algorithm is proposed in this study. Firstly, VMD is used to decompose PL into several smooth subsequences with different frequency scales. Then, a LSTM−based model for each subsequence is built to predict the corresponding subsequence. In addition, PSO is used to optimize the hyperparameters of each LSTM prediction model. Finally, the predicted subsequences are reconstructed to obtain the final PL prediction results. The performance of the VMD−PSO−LSTM method is verified by combining the measured PL. The minimum RMSE and MAE indicators for the VMD−PSO−PSTM method are 0.368 and 0.276, respectively. The percentage improvement of prediction performance compared to other prediction methods can reach at most 72.46 and 77.61% in RMSE and MAE, respectively, showing that the VMD−PSO−LSTM method has the advantages of high accuracy and outperforms other comparison methods

Robust bus bridging service design under rail transit system disruptions

This paper focuses on designing robust bus bridging service in response to the rail transit system disruptions. We firstly develop a path-based multi-commodity flow formulation to bus bridging service design. Then its robust counterpart is formulated to incorporate bus travel time uncertainty. The column generation procedure is devised to solve this problem efficiently. At last, we carry out case studies to demonstrate its applicability and promising effects. The results reveal that our approach can significantly reduce the total cost and number of stranded passengers in disruption events. Besides, the rise of bus travel time variation will deteriorate the performance of bus bridging service

Direct Nitridation Synthesis of Quasi-Spherical β-Si3N4 Powders with CaF2 Additive

In this work, the quasi-spherical β-Si3N4 powders were synthesized via an efficient direct nitridation strategy with CaF2 as the catalytic material under NH3 atmosphere. The effect of CaF2 on phase composition and crystalline morphology was studied. CaF2 additive can accelerate the nitridation of silicon powders, and the particles of nitridation products tend to have an equiaxed structure with the CaF2 additive increasing. When 4 wt% CaF2 additive or more was added, submicron β-Si3N4 particles with quasi-spherical morphology and eminent crystal integrity were obtained. In contrast, irregular α-Si3N4 particles appear as the main phase with less than 4 wt% CaF2 additive. The growth mechanism of Si3N4 particles was also discussed. CaxSiyOz liquid phase is crucial in the nitridation of silicon powders with CaF2 additive

Element Geochemical Characteristics and Provenance Conditions of the 1st Member of Jurassic Zhongjiangou Formation in Wudun Sag, Dunhuang Basin

In order to clarify the provenance of the 1st member of the Zhongjiangou formation in Wudun sag, Dunhuang basin, the structural attributes, weathering and sedimentary characteristics of the provenance area were analyzed by means of element geochemistry, so as to determine the differences of sediment sources in different well areas. The results show that the higher the Al2O3 and K2O contents, the higher the enrichment of large ion lithophile elements and high field strength elements, while the iron and magnesium elements are relatively deficient, and there are characteristics of medium degree differentiation of light and heavy rare earth elements in Well XC1 and Well D2. The lower the Al2O3 content and the higher the SiO2 content, a loss of large ion lithophile elements and high field strength elements are observed, while the ferrophilic magnesium elements show serious loss, as shown in the characteristics of the high degree of differentiation of light and heavy rare earth elements in Well D1. In the UCC-normalized element spidergrams, the trend of Well XC1 and Well D2 is similar, which is different from that of well D1, indicating that the sediments of Well XC1 and Well D2 come from the same provenance area, while the sediment of Well D1 comes from a different provenance area. The provenance area of Well XC1 and Well D2 shows strong tectonic activity and strong weathering, while the provenance area of well D1 exhibits relatively weak tectonic activity and weathering. Combined with previous research results, Wudun sag is mainly characterized by a faulted lacustrine basin controlled by the southern boundary fault in the Jurassic layer. Therefore, the sediments of Well XC1 and Well D2 mainly come from the southern Sanweishan uplift provenance area, with strong tectonic activity; the sediments of Well D1 mainly come from the northern Beishan provenance area, with relatively weak tectonic activity

Full-Coupled Convolutional Transformer for Surface-Based Duct Refractivity Inversion

A surface-based duct (SBD) is an abnormal atmospheric structure with a low probability of occurrence buta strong ability to trap electromagnetic waves. However, the existing research is based on the assumption that the range direction of the surface duct is homogeneous, which will lead to low productivity and large errors when applied in a real-marine environment. To alleviate these issues, we propose a framework for the inversion of inhomogeneous SBD M-profile based on a full-coupled convolutional Transformer (FCCT) deep learning network. We first designed a one-dimensional residual dilated causal convolution autoencoder to extract the feature representations from a high-dimension range direction inhomogeneous M-profile. Second, to improve efficiency and precision, we proposed a full-coupled convolutional Transformer (FCCT) that incorporated dilated causal convolutional layers to gain exponentially receptive field growth of the M-profile and help Transformer-like models improve the receptive field of each range direction inhomogeneous SBD M-profile information. We tested our proposed method performance on two sets of simulated sea clutter power data where the inversion of the simulated data reached 96.99% and 97.69%, which outperformed the existing baseline methods

A Multi-Epitope Fusion Protein-Based p-ELISA Method for Diagnosing Bovine and Goat Brucellosis.

In recent years, the incidence of brucellosis has increased annually, causing tremendous economic losses to animal husbandry in a lot of countries. Therefore, developing rapid, sensitive, and specific diagnostic techniques is critical to control the spread of brucellosis. In this study, bioinformatics technology was used to predict the B cell epitopes of the main outer membrane proteins of Brucella, and the diagnostic efficacy of each epitope was verified by an indirect enzyme-linked immunosorbent assay (iELISA). Then, a fusion protein containing 22 verified epitopes was prokaryotically expressed and used as an antigen in paper-based ELISA (p-ELISA) for serodiagnosis of brucellosis. The multi-epitope-based p-ELISA was evaluated using a collection of brucellosis-positive and -negative sera collected from bovine and goat, respectively. Receiver operating characteristic (ROC) curve analysis showed that the sensitivity and specificity of detection-ELISA in diagnosing goat brucellosis were 98.85 and 98.51%. The positive and the negative predictive values were 99.29 and 98.15%, respectively. In diagnosing bovine brucellosis, the sensitivity and specificity of this method were 97.85 and 96.61%, with the positive and negative predictive values being identified as 98.28 and 97.33%, respectively. This study demonstrated that the B cell epitopes contained in major antigenic proteins of Brucella can be a very useful antigen source in developing a highly sensitive and specific method for serodiagnosis of brucellosis