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Purpose Classification and grading of central nervous system (CNS) tumours play a critical role in the clinic. When WHO CNS5 simplifies the histopathology diagnosis and places greater emphasis on molecular pathology, artificial intelligence (AI) has been widely used to meet the increased need for an automatic histopathology scheme that could liberate pathologists from laborious work. This study was to explore the diagnosis scope and practicality of AI. Methods A one-stop Histopathology Auxiliary System for Brain tumours (HAS-Bt) is introduced based on a pipeline-structured multiple instance learning (pMIL) framework developed with 1,385,163 patches from 1,038 hematoxylin and eosin (H&E) slides. The system provides a streamlined service including slide scanning, whole-slide image (WSI) analysis and information management. A logical algorithm is used when molecular profiles are available. Results The pMIL achieved an accuracy of 0.94 in a 9-type classification task on an independent dataset composed of 268 H&E slides. Three auxiliary functions are developed and a built-in decision tree with multiple molecular markers is used to automatically formed integrated diagnosis. The processing efficiency was 443.0 seconds per slide. Conclusion HAS-Bt shows outstanding performance and provides a novel aid for the integrated neuropathological diagnostic workflow of brain tumours using CNS 5 pipeline.

Ischemic stroke is the most common type of stroke, ranked as the second leading cause of death worldwide. The Alberta Stroke Program Early CT Score (ASPECTS) is considered as a systematic method of assessing ischemic change on non-contrast CT scans (NCCT) of acute ischemic stroke (AIS) patients, while still suffering from the requirement of experts' experience and also the inconsistent results between readers. In this study, we proposed an automated ASPECTS method to utilize the powerful learning ability of neural networks for objectively scoring CT scans of AIS patients. First, we proposed to use the CT perfusion (CTP) from one-stop stroke imaging to provide the golden standard of ischemic regions for ASPECTS scoring. Second, we designed an asymmetry network to capture features when comparing the left and right sides for each ASPECTS region to estimate its ischemic status. Third, we performed experiments in a large main dataset of 870 patients, as well as an independent testing dataset consisting of 207 patients with radiologists' scorings. Experimental results show that our network achieved remarkable performance, as sensitivity and accuracy of 93.7 and 92.4% in the main dataset, and 95.5 and 91.3% in the independent testing dataset, respectively. In the latter dataset, our analysis revealed a high positive correlation between the ASPECTS score and the prognosis of patients in 90DmRs. Also, we found ASPECTS score is a good indicator of the size of CTP core volume of an infraction. The proposed method shows its potential for automated ASPECTS scoring on NCCT images.

The BDS-3 has been officially commissioned since July 31, 2020. As one of the seven services of BDS-3, precision point positioning is free to users in China and its surrounding areas. In order to assess the performance of BDS-3 PPP service, the observation data from eight domestic stations were analyzed in terms of the accuracy of corrections, the coverage of service, the positioning accuracy, and the convergence time. The coverage of service was assessed based on the combination of the number of effectively corrected satellites and the DOP values, and the convergence time was calculated using a slide-and-restart method. The results prove that: 1) The averages of signal in space ranging error (SISRE) (RMS) are 0.10 m and 0.14 m for BDS-3 and GPS respectively. 2) The availabilities of PPP service of BDS-3 only and BDS-3&GPS dual system are both higher than 90% in China and its surrounding areas. 3) The average convergence time of the kinematic PPP of BDS-3 is 17 min (0.3 m horizontally and 0.6 m vertically). After convergence, the average accuracy (95% confidence level) reaches 0.17 m horizontally and 0.26 m vertically. The average convergence time of the kinematic PPP of BDS-3&GPS dual system is 10 min (0.2 m horizontally and 0.4 m vertically). After convergence, the average accuracy (95% confidence level) reaches 0.11 m horizontally and 0.22 m vertically.

Medical images routinely acquired in clinical facilities are mostly low resolution (LR), in consideration of acquisition time and efficiency. This renders challenging for clinical diagnosis of hippocampal sclerosis where additional sequences for hippocampus need to be acquired. In contrast, high-resolution (HR) images provide more detailed information for disease investigation. Recently, image super-resolution (SR) methods were proposed to reconstruct HR images from LR inputs. However, current SR methods generally use simulated LR images and intensity constraints, which limit their applications in clinical practice. To solve this problem, we utilized real paired LR and HR images and trained a Structure-Constrained Super Resolution (SCSR) network. First, we proposed a single image super-resolution framework where mixed loss functions were introduced to enhance the reconstruction of brain tissue boundaries besides intensity constraints; Second, since the structure hippocampus is relatively small, we further proposed a weight map to enhance the reconstruction of subcortical regions. Experimental results using 642 real paired cases showed that the proposed method outperformed the the-state-of-the-art methods in terms of image quality with a PSNR of 27.0405 and an SSIM of 0.9958. Also, experiments using Radiomics features extracted from hippocampus on SR images obtained through the proposed method achieved the best accuracy of 95% for differentiating subjects with left and right hippocampal sclerosis from normal controls. The proposed method shows its potential for disease screening using clinical routine images.

In order to analyze the network structure of the “East Turkistan” organization and identify members with higher individual threats, so as to conduct a better attack on the “East Turkistan” organization. We use Gephi, a network analysis software, and collect open source information on three batches of 25 terrorists announced by the Ministry of Public Security and use networkX, a Python package to study the structure of complex networks, to conduct social network analysis(SNA) of the “East Turkistan” organization personnel. The “East Turkistan” organization presents a combination of scale-free network and hierarchical network in the topological structure. To manifest the analysis of social network methods' importance in combating terrorism, we simulate the effect of removing the core members of the “East Turkistan” organization. The construction of SNA requires a large deal of open source information. How to obtain intelligence is an important factor that restricts precision strikes. In addition, how to combine social network analysis with computer deep learning knowledge is also a new hot spot and direction in the field of anti-terrorism.

Binding of the proteins human lactoferrin (LF) and human bone morphogenetic protein-2 (BMP2) to a hydroxylated TiO2 rutile (110) surface has been modeled using molecular dynamics (MD) simulations. In order to study the effect of the hydrophobicity of the rutile surface on the protein binding process, the rutile surface was made more hydrophilic or more hydrophobic by adjusting the rutile atomic charges. The binding of LF and BMP2 to the hydrophobic rutile surface occurred through direct contact between the protein and rutile via both hydrophobic and hydrophilic amino acids. This forced the proteins to undergo structural rearrangements, observed primarily in BMP2. Binding to the hydrophilic rutile surface was largely indirect via the hydration layer of water on the surface of rutile. Both LF and BMP2 had a higher binding strength to the hydrophobic rutile surfaces than to the hydrophilic surfaces, as seen in the larger amplitude of the binding energies.

The interactions between proteins and functional biomaterials under different physical and environmental conditions need to be understood when designing biomedical devices. Herein, we present a molecular dynamics simulation study of the fragment antigen-binding (Fab) of trastuzumab (a monoclonal antibody) and its complex with a peptide-modified polyvinyl alcohol (PVA) hydrogel at different pH values. Consistent with experiments, PVA when modified by charged ligands does shrink as a direct response to a drop in the pH. The protein maintains a stable conformation when adsorbed on the hydrogel matrix with a varied pH, showing no signs of denaturation in all simulated systems, suggesting that peptide-grafted PVA is a good biocompatible material. Under neutral conditions, the hydrogel alone stabilizes the interactions between the protein and the peptide ligands. Strikingly under acidic conditions the protein–ligand interactions are disrupted by a collective protonation of ligands. A sharp decrease in the interaction energies, accompanied by the sudden increase of the protein–ligand distance, indicates a rapid pH response in the protein–hydrogel complex. This will be important in protein delivery and purification. The effect of pH on the interactions and the dynamics of the protein and the sudden pH response of the hydrogel at the atomic level present a new functional perspective in developing new hydrogels with desirable properties.

Additional file 2: Fig. S1. Density distribution of lncRNAs on eight chromosomes of M. truncatula seedlings.

Additional file 5: Fig. S4. Characteristics of cold-responsive mRNAs in M. truncatula seedlings.

Additional file 3: Fig. S2. Number and density distribution of lncRNAs on chromosome of M. truncatula seedlings with and without cold treatment.

Additional file 7: Fig. S6. Phylogenetic tree of MtCBFs and AtCBFs was constructed by MEGA.

Additional file 1: Table S1. Statistical date of the RNA-Seq quality for experimental samples. Table S2. Correlation coefficient of lncRNAs for experimental samples. Table S3. Correlation coefficient of mRNAs for experimental samples. Table S4. Statistical date of the RNA-Seq results mapped to M. truncatula A17 genome for experimental samples. Table S5. GO enhancements of the putative targets of cold-responsive lncRNAs in leaves of M. truncatula seedlings. Table S6. GO enhancements of the putative targets of cold-responsive lncRNAs in roots of M. truncatula seedlings. Table S7. Primer sequences used for real-time quantitative RT-PCR.

Additional file 4: Fig. S3. Identification of mRNAs by high-throughput sequencing in M. truncatula seedlings.

Signal power level of GNSS satellites is key parameter for service performance. The relationship between navigation signal power and carrier-to-noise ratio (CNR) of GNSS receiver is described. The time variation characteristics of CNR for one station and satellite pair during 10 days and also characteristics between different stations is analyzed. Results show that the relationship between CNR and elevation angle is consistent and repeatable for a specified signal of one station and satellite pair during that 10 days. Then a model related CNR with elevation angle is established based on global observation data. Using the model, CNR changing trend could be predicted, and so its anomaly could be monitored. For more efficient monitoring, we use a mapping method to transform the CNR at different elevation angles to its corresponding value of 90-degree elevation angle. Finally, data from global tracking network are used to validate the feasibility and reliability of this approach. The results show that there are signal power changes for GPS satellites occurred during April 13th 2018 UTC.

Additional file 6: Fig. S5. Protein sequence alignment of the MtCBFs and AtCBFs was constructed by MAGE.

Color is an important trait of pearls which significantly affects their economic value. Golden seawater pearls with excellent luster are becoming popular among consumers, but golden freshwater pearls are rare. Hyriopsis cumingii is the most important freshwater mussel for pearl production in China and a selective breeding program has recently been developed to improve the yellowness and luster of the inner shell of H. cumingii for better production of golden freshwater pearls. As part of this breeding program, it has been necessary to evaluate the genetic parameters of the target traits and possible genotype-by-environment interactions. In this study, we investigated the effects of the environment on the genetic parameters of the inner shell color and luster of F1 golden strain H. cumingii, reared at two sites (Chongming and Wuyi). Inner shell color parameters and inner shell luster were recorded in 570 19-month-old individuals. Microsatellite-based DNA parentage analysis was used to assign the mussels to 19 maternal half-sib families, including 41 full-sib families. The results showed that the estimated heritability of the inner shell color parameters L*, a*, b* and dE were low, 0.13 ± 0.04, 0.14 ± 0.04, 0.12 ± 0.03 and 0.14 ± 0.05, respectively. The heritability of inner shell luster was medium (0.28 ± 0.07). The correlations between the genetic and phenotypic parameters for inner shell color and inner shell luster were insignificant at less than 0.8. Genetic correlations between the inner shell color parameters and inner shell luster in Chongming and Wuyi ranged from −0.07 ± 0.28 to −0.37 ± 0.24 and showed significant genotype-by-environment interaction. This study demonstrated that inner shell color parameters and inner shell luster could be improved by selective breeding. Environmental influences on genotype for inner shell color and inner shell luster should be taken into account in future selective breeding programs in golden strain H. cumingii.

Water quality estimation tools based on real-time monitoring are essential for the effective management of organic pollution in watersheds. This study aims to monitor changes in the levels of chemical oxygen demand (COD, CODMn) and dissolved organic matter (DOM) in Erhai Lake Basin, exploring their relationships and the ability of DOM to estimate COD and CODMn. Excitation emission matrix–parallel factor analysis (EEM–PARAFAC) of DOM identified protein-like component (C1) and humic-like components (C2, C3, C4). Combined with random forest (RF), maximum fluorescence intensity (Fmax) values of components were selected as estimation parameters to establish models. Results proved that the COD of rivers was more sensitive to the reduction in C1 and C2, while CODMn was more sensitive to C4. The DOM of Erhai Lake thrived by internal sources, and the relationship between COD, CODMn, and DOM of Erhai Lake was more complicated than rivers (inflow rivers of Erhai Lake). Models for rivers achieved good estimations, and by adding dissolved oxygen and water temperature, the estimation ability of COD models for Erhai Lake was significantly improved. This study demonstrates that DOM-based machine learning can be used as an alternative tool for real-time monitoring of organic pollution and deepening the understanding of the relationship between COD, CODMn, and DOM, and provide a scientific basis for water quality management.

We designed a series of porous graphene as the separation membrane for hydrogen gas in coal gas. The permeation process of different gas molecules (H2, CO, CH4, and H2S) in porous graphene was evaluated under the atmospheric pressure and high pressure conditions. Our results indicate the hydrogen permeability and selectivity could be tuned by the size and the shape of the porous graphene. For graphene with bigger pores, the selectivity for hydrogen gas could decrease. In the porous graphene with same pore area, the hydrogen gas selectivity could be affected by the shape of the pore. The potential of mean force (PMF) of different gases to pass through a good separation candidate was calculated. The order of PMF for different gases to pass through the good separation candidate is H2 < CO < CH4 ≈ H2S, which is also confirmed by the first-principle density function theory (DFT) calculation.

The improvement of the resolution of DNA sequencing by nanopore technology is very important for its real-life application. In this paper, we report our work on using molecular dynamics simulation to study the dependence of DNA sequencing on the translocation time of DNA through a graphene nanopore, using the single-strand DNA fragment translocation through graphene nanopores with diameters down to ∼2 nm as examples. We found that A, T, C, and G could be identified by the difference in the translocation time between different types of nucleotides through 2 nm graphene nanopores. In particular, the recognition of the graphene nanopore for different nucleotides can be greatly enhanced in a low electric field. Our study suggests that the recognition of a graphene nanopore by different nucleotides is the key factor for sequencing DNA by translocation time. Our study also indicates that the surface of a graphene nanopore can be modified to increase the recognition of nucleotides and to improve the resolution of DNA sequencing based on the DNA translocation time with a suitable electric field.

Understanding the mechanism and pathway of anti-cancer drugs to be pumped out by P-glycoprotein (P-gp) in cancer cell is very important for the successful chemotherapy. P-gp is a member of ATP-binding cassette (ABC) transporters. In this study, random accelerated molecular dynamics (RAMD) simulation was used to explore the potential egress pathway of ligands from the binding pocket. This could be considered as a reverse process of drug binding. The most possible portal of drugs to dissociate is TM4/TM6, which is almost the same for different drugs, such as paclitaxel and doxorubicin. The interactions in the binding site are found to be remarkably stronger than that outside of the binding site. The results were suggested by the free energy calculation between P-gp and different drugs from metadynamics simulation. All the results indicate that the flexibility of inner residues, especially the residue Phe339, is very important for the drugs to access the binding site.

Understanding of the mechanism and dynamics of DNA loading into carbon nanotubes (CNTs) is very important for the promising applications of CNTs in DNA sequencing, drug delivery and gene delivery systems etc. In this work, the loading mechanism and dynamics of different ssDNA oligomers into single-walled carbon nanotubes (SWNTs) was investigated through molecular dynamics simulations, steered molecular dynamics simulation and binding free energy calculations. Our simulation results showed that the loading of different ssDNA oligomers into the zigzag SWNT is much easier than for the armchair SWNT. Confined in both zigzag and armchair type SWNTs, ssDNA oligomers have a helical structure and their bases adapt the orientation parallel to the interior wall. From detailed analysis of the interaction energy, potential of mean force (PMF) of the unloading process and nucleotide binding free energy, our results show that the chirality of SWNTs has a large effect on the binding strength of nucleotides, and hence affects the loading dynamics of ssDNA into SWNTs.

Permafrost thaw alters the physical and environmental conditions of soil and may thus cause a positive feedback to climate warming through increased methane emissions. However, the current knowledge of methane emissions following thermokarst development is primarily based on expanding lakes and wetlands, with upland thermokarst being studied less often. In this study, we monitored the methane emissions during the peak growing seasons of two consecutive years along a thaw sequence within a thermo-erosion gully in a Tibetan swamp meadow. Both years had consistent results, with the early and midthaw stages (3 to 12 years since thaw) exhibiting low methane emissions that were similar to those in the undisturbed meadow, while the emissions from the late thaw stage (20 years since thaw) were 3.5 times higher. Our results also showed that the soil water-filled pore space, rather than the soil moisture per se, in combination with the sand content, were the main factors that caused increased methane emissions. These findings differ from the traditional view that upland thermokarst could reduce methane emissions owing to the improvement of drainage conditions, suggesting that upland thermokarst development does not always result in a decrease in methane emissions.

Seed germination is sensitive to salt stress. ABA and Ca2+ are involved in the regulation of seed germination under salt stress. Ca2+ influx mediated by glutamate receptors (GLRs) plays important roles in many physiological processes in plants. Here, we investigated the correlation of GLRs, Ca2+ and ABA during seed germination in response to salt stress by using Arabidopsis thaliana wild-type and T-DNA insertion knockout mutants of glutamate receptor homolog3.4. We demonstrated that atglr3.4-1 and atglr3.4-2 mutants were more sensitive to NaCl during seed germination and post-germination growth than wild-type plants. Treatments of wild-type seedlings with NaCl evoked a marked elevation in cytosolic Ca2+ activity ([Ca2+]cyt), and the elevation was inhibited by antagonists of GLRs, while the NaCl-induced elevation in [Ca2+]cyt was impaired in atglr3.4-1 and atglr3.4-2 mutants. Moreover, the mutants exhibited a lower expression of SOS3, SOS2 and SOS1, and greater accumulation of Na+ than wild-type seeds in the presence of NaCl. Mutation of AtGLR3.4 rendered the mutants more sensitive to ABA, while overexpression of AtGLR3.4 made the transgenic lines more tolerant to ABA in terms of seed germination. However, there was no difference in ABA content between atglr3.4 mutants and wild-type seeds, accompanied by lower expression of ABI3 and ABI4 in atglr3.4 mutants when challenged with NaCl. These results demonstrate that AtGLR3.4-mediated Ca2+ influx may be involved in the regulation of seed germination under salt stress by modulating Na+ accumulation through the SOS pathway.

In the past years, skin beautifying softwares have been widely used in portable devices for social activities, which have the functionalities of turning one's skin into flawless complexion. With a huge number of photos uploaded to social media, it is useful for users to distinguish whether a photo is beautified or not. To address this problem, in this paper, we propose a skin beautification detection method by mining and distinguishing the intrinsic features of original photos and the corresponding beautified photos. To this aim, we propose to use sparse coding to learn two sets of basis functions using densely sampled patches from the original photos and the beautified photos, respectively. To detect whether a test photo is beautified, we represent the sampled patches from the photo using the learned basis functions and then see which set of basis functions produces more sparse coefficients. To our knowledge, our effort is the first one to detect skin beautification. To validate the effectiveness of the proposed method, we collected about 1000 photos including both the original photos and the photos beautified by a software. Our experimental results indicate the proposed method achieved a desired detection accuracy of over 80%.

Understanding of protein-ligand interactions and its influences on protein stability is necessary in the research on all biological processes and correlative applications, for instance, the appropriate affinity ligand design for the purification of bio-drugs. In this study, computational methods were applied to identify binding site interaction details between trastuzumab and its natural receptor. Trastuzumab is an approved antibody used in the treatment of human breast cancer for patients whose tumors overexpress the HER2 (human epidermal growth factor receptor 2) protein. However, rational design of affinity ligands to keep the stability of protein during the binding process is still a challenge. Herein, molecular simulations and quantum mechanics were used on protein-ligand interaction analysis and protein ligand design. We analyzed the structure of the HER2-trastuzumab complex by molecular dynamics (MD) simulations. The interaction energies of the mutated peptides indicate that trastuzumab binds to ligand through electrostatic and hydrophobic interactions. Quantitative investigation of interactions shows that electrostatic interactions play the most important role in the binding of the peptide ligand. Prime/MM-GBSA calculations were carried out to predict the binding affinity of the designed peptide ligands. A high binding affinity and specificity peptide ligand is designed rationally with equivalent interaction energy to the wild-type octadecapeptide. The results offer new insights into affinity ligand design.

Applications of graphene sheets in the fields of biosensors and biomedical devices are limited by the aqueous solubility of graphene. Consequently, understanding the role of water molecules in the aggregation or dispersion of graphene in aqueous solution with a biomolecule is of vital importance to its application. Herein, protein is spontaneously released by the layer-to-layer aggregation of two single-layer graphene sheets due to van der Waals force between the sheets. The properties of water molecules, including density and dynamics, are discussed in detail. The dynamic behavior of aggregation of graphene sheets is triggered by the dynamics of water molecules. To stabilize dispersed graphene sheets in aqueous solution, the density of water molecules between the graphene sheets should be larger than 0.83 g cm(-3), and graphene modified by hydroxyl groups could be a good choice. The stability of a model protein on the graphene sheet is studied to investigate the biological compatibility of graphene sheets. To be a material with good biocompatibility, graphene should be functionalized by hydrophilic groups. The results presented herein could be helpful in the research and application of graphene sheets in the fields of biomaterials, biosensors, and biomedical devices.

Grand canonical Monte Carlo (GCMC) simulations were employed to compute the adsorption of hydrogen gas in zeolitic imidazolate frameworks (ZIFs). This set of ZIFs has the same basic structure, but different functional groups or central metal ions were substituted into the zeolitic framework. The amount of hydrogen adsorption in ZIFs is mainly affected by the effective porosity, but the substituent group may also influence the hydrogen adsorption amount. Under both low pressure and high pressure conditions, the trend in hydrogen adsorption amount is: –NO 2 >–CN (–Cl)>–CH 3 , which suggests a role for the electronegativity of the substituted functional group. Generally speaking, the higher the electronegativity, the larger the adsorption amount. However, the –C 6 H 4 group is a special functional group with high π-electron delocalization that can greatly increase the saturated adsorption amount by ZIFs. The effect of the benzene (–C 6 H 4 group) fused to imidazole in the large pores of ZIFs is superior relative to that in the small pores. The central metal ion, Zn(II) or Co(II), plays a negligible role in the ZIFs saturation adsorption capacity. These results may be helpful in designing and synthesizing new ZIFs with higher hydrogen uptake capability.

Insulin-like growth factor binding protein 7 (IGFBP7) can bind to insulin with high affinity which inhibits the early steps of insulin action. Lack of recognition mechanism impairs our understanding of insulin regulation before it binds to insulin receptor. Here we combine computational simulations with experimental methods to investigate the interaction between IGFBP7 and insulin. Molecular dynamics simulations indicated that His200 and Arg198 in IGFBP7 were key residues. Verified by experimental data, the interaction remained strong in single mutation systems R198E and H200F but became weak in double mutation system R198E-H200F relative to that in wild-type IGFBP7. The results and methods in present study could be adopted in future research of discovery of drugs by disrupting protein–protein interactions in insulin signaling. Nevertheless, the accuracy, reproducibility and costs of free-energy calculation are still problems that need to be addressed before computational methods can become standard binding prediction tools in discovery pipelines.

P-glycoprotein (P-gp) pumps a broad range of structurally diverse anti-cancer drugs out of cancer cells. Therefore, multi-drug resistance (MDR) in chemotherapy closely correlates with P-gp. However, how this single transport system recognizes different substrates remains unclear. In this study, we attempt to uncover the mechanism of substrate promiscuity of P-gp by atomistic molecular dynamics simulations. Results indicate that different drugs like paclitaxel and doxorubicin approach the putative binding site of P-gp, and the inner residues are found to be important in this process. An obstacle-overcoming process was observed, illustrating that the inner residues are flexible. Interaction energy calculations suggest that the inner residues possess high affinity toward substrates. The cavity of adaptability to accommodate different drugs would help explain why P-gp has so many different substrates.

From a traditional point of view, a country’s diplomatic position and principles are determined by the political environment and military circumstances. The global ecological environment, which has worsened since 1970s, is now paid great attention to by people all over the world. The eco-environmental problem has gradually become one of the most important factors that affect the international relations nowadays. Besides nontraditional diplomacy in culture, trade and security aspects, China is now carving out a new way in nontraditional diplomacy---ecological diplomacy. It greatly impels China to break through the traditional diplomatic thinking and try a new diplomatic strategy.