CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution.

Recently, deep convolution neural networks (CNNs) steered face super-resolution methods have achieved great progress in restoring degraded facial details by joint training with facial priors. However, these methods have some obvious limitations. On the one hand, multi-task joint learning requires additional marking on the dataset, and the introduced prior network will significantly increase the computational cost of the model. On the other hand, the limited receptive field of CNN will reduce the fidelity and naturalness of the reconstructed facial images, resulting in suboptimal reconstructed images. In this work, we propose an efficient CNN-Transformer Cooperation Network (CTCNet) for face super-resolution tasks, which uses the multi-scale connected encoder-decoder architecture as the backbone. Specifically, we first devise a novel Local-Global Feature Cooperation Module (LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a Transformer block, to promote the consistency of local facial detail and global facial structure restoration simultaneously. Then, we design an efficient Feature Refinement Module (FRM) to enhance the encoded features. Finally, to further improve the restoration of fine facial details, we present a Multi-scale Feature Fusion Unit (MFFU) to adaptively fuse the features from different stages in the encoder procedure. Extensive evaluations on various datasets have assessed that the proposed CTCNet can outperform other state-of-the-art methods significantly. Source code will be available at https://github.com/IVIPLab/CTCNet.

A near infrared fluorescent probe for rapid sensing of peroxynitrite in living cells and breast cancer mice.

Peroxynitrite (ONOO-) plays an essential role in numerous physiological and pathological processes owing to its strong oxidation and nitrification. Many studies have shown that ONOO- abnormalities are associated with inflammatory diseases, even cancer, such as arthritis, hepatitis, pneumonia, and breast cancer. Thus, developing a trustworthy technology to monitor ONOO- levels is critical in inflammatory or cancer illnesses. Herein, an ultrafast near-infrared (NIR) fluorescent probe (Cy-OH-ONOO) is proposed to detect ONOO- within 30 s. The probe's borate moiety is oxidized and separated from Cy-OH-ONOO, releasing a NIR fluorescence signal after interacting with ONOO- under physiological circumstances. In addition, the probe displays good selectivity and sensitivity towards ONOO- compared to other related biological species. Moreover, it is applied to the image and detects the level fluctuation of ONOO- in living cells and breast cancer mice based on excellent features with high biocompatibility and low toxicity of the developed probe. Therefore, Cy-OH-ONOO could serve as a powerful imaging tool to understand the correlation of ONOO- with inflammatory or breast cancer pathophysiological processes and to assess ONOO- levels in cellular oxidative stress.

Successful Management of Late Sinus Graft Infection via Functional Endoscopic Sinus Surgery and Press-Fit Block Bone Graft: A Case Report.

The purpose of this case report is to feature an interesting case where a staged approach was used to manage a failed implant site that led to a late sinus graft infection and sinusitis with an oroantral fistula (OAF), by using functional endoscopic sinus surgery (FESS) and an intraoral press-fit block bone graft technique. Sixteen years ago, a 60-year-old female patient underwent maxillary sinus augmentation (MSA) with 3 implants placed simultaneously in the right atrophic ridge. However, No. 3 and 4 implants were removed due to advanced peri-implantitis. The patient later developed purulent discharge from the site, headache, and complained of air leakage due to an OAF. The patient was referred to an otolaryngologist for FESS to treat the sinusitis. Two months after FESS, the sinus was re-entered. Residual inflammatory tissues and necrotic graft particles in the OAF site were removed. A block bone harvested from the maxillary tuberosity was press-fitted to the OAF site and grafted. After 4 months of grafting, the grafted bone was well incorporated with the surrounding native bone. Two implants were successfully placed in the grafted site with good initial stability. The prosthesis was delivered 6 months after implant placement. After the 2 years of follow-up, patient was functioning well without sinus complications. Within limitation of this case report, the staged approach via FESS and intraoral press-fit block bone graft is an effective method that can be used to successfully manage OAF and vertical defects at the implant site.

Zirconium and Yttrium Co-Doped BaCo0.8Zr0.1Y0.1O3-δ: A New Mixed-Conducting Perovskite Oxide-Based Membrane for Efficient and Stable Oxygen Permeation.

Oxygen permeation membranes (OPMs) are regarded as promising technology for pure oxygen production. Among various materials for OPMs, perovskite oxides with mixed electron and oxygen-ion (e-/O2-) conducting capability have attracted particular interest because of the high O2- conductivity and structural/compositional flexibility. However, BaCoO3-δ-based perovskites as one of the most investigated OPMs suffer from low oxygen permeation rate and inferior structural stability in CO2-containing atmospheres. Herein, zirconium and yttrium co-doped BaCoO3-δ (BaCo1-2xZrxYxO3-δ, x = 0, 0.05, 0.1 and 0.15) are designed and developed for efficient and stable OPMs by stabilizing the crystal structure of BaCoO3-δ. With the increased Zr/Y co-doping content, the crystal structural stability of doped BaCoO3-δ is much improved although the oxygen permeation flux is slightly reduced. After optimizing the co-doping amount, BaCo0.8Zr0.1Y0.1O3-δ displays both a high rate and superior durability for oxygen permeation due to the well-balanced grain size, oxygen-ion mobility, crystal structural stability, oxygen vacancy concentration and surface exchange/bulk diffusion capability. Consequently, the BaCo0.8Zr0.1Y0.1O3-δ membrane delivers a high oxygen permeation rate of 1.3 mL min-1 cm-2 and relatively stable operation at 800 ∘C for 100 h. This work presents a promising co-doping strategy to boost the performance of perovskite-based OPMs, which can promote the industrial application of OPM technology.

Reconstruction and analysis of genome-scale metabolic model for thermophilic fungus Myceliophthora thermophila.

Myceliophthora thermophila, a thermophilic fungus that can degrade and utilize all major polysaccharides in plant biomass, has great potential in biotechnological industries. Here, the first manually curated genome-scale metabolic model iDL1450 for M. thermophila was reconstructed using an autogenerating pipeline with thorough manual curation. The model contains 1450 genes, 2592 reactions, and 1784 unique metabolites. High accuracy was shown in predictions related to carbon and nitrogen source utilization based on data obtained from Biolog experiments. Besides, metabolism profiles were analyzed using iDL1450 integrated with transcriptomics data of M. thermophila at various growth temperatures. The refined model provides new insights into thermophilic fungi metabolism and sheds light on model-driven strain design to improve biotechnological applications of this thermophilic lignocellulosic fungus.

Bifunctional Single-Atom Cobalt Electrocatalysts with Dense Active Sites Prepared via a Silica Xerogel Strategy for Rechargeable Zinc-Air Batteries.

The N-doped cobalt-based (Co) bifunctional single atom catalyst (SAC) has emerged as one of the most promising candidates to substitute noble metal-based catalysts for highly efficient bifunctionality. Herein, a facile silica xerogel strategy is elaborately designed to synthesize uniformly dispersed and dense Co-Nx active sites on N-doped highly porous carbon networks (Co-N-C SAC) using economic biomass materials. This strategy promotes the generation of massive mesopores and micropores for substantially improving the formation of Co-Nx moieties and unique network architecture. The Co-N-C SAC electrocatalysts exhibit an excellent bifunctional activity with a potential gap (ΔE) of 0.81 V in alkaline medias, outperforming those of the most highly active bifunctional electrocatalysts. On top of that, Co-N-C SAC also possesses outstanding performance in ZABs with superior power density/specific capacity. This proposed synthetic method will provide a new inspiration for fabricating various high-content SACs for varied applications.

Compensating Complete Loss of Signal Recognition Particle During Co-translational Protein Targeting by the Translation Speed and Accuracy.

Signal recognition particle (SRP) is critical for delivering co-translational proteins to the bacterial inner membrane. Previously, we identified SRP suppressors in Escherichia coli that inhibit translation initiation and elongation, which provided insights into the mechanism of bypassing the requirement of SRP. Suppressor mutations tended to be located in regions that govern protein translation under evolutionary pressure. To test this hypothesis, we re-executed the suppressor screening of SRP. Here, we isolated a novel SRP suppressor mutation located in the Shine-Dalgarno sequence of the S10 operon, which partially offset the targeting defects of SRP-dependent proteins. We found that the suppressor mutation decreased the protein translation rate, which extended the time window of protein targeting. This increased the possibility of the correct localization of inner membrane proteins. Furthermore, the fidelity of translation was decreased in suppressor cells, suggesting that the quality control of translation was inactivated to provide an advantage in tolerating toxicity caused by the loss of SRP. Our results demonstrated that the inefficient protein targeting due to SRP deletion can be rescued through modulating translational speed and accuracy.

Signal Recognition Particle Suppressor Screening Reveals the Regulation of Membrane Protein Targeting by the Translation Rate.

The signal recognition particle (SRP) is conserved in all living organisms, and it cotranslationally delivers proteins to the inner membrane or endoplasmic reticulum. Recently, SRP loss was found not to be lethal in either the eukaryote Saccharomyces cerevisiae or the prokaryote Streptococcus mutans In Escherichia coli, the role of SRP in mediating inner membrane protein (IMP) targeting has long been studied. However, the essentiality of SRP remains a controversial topic, partly hindered by the lack of strains in which SRP is completely absent. Here we show that the SRP was nonessential in E. coli by suppressor screening. We identified two classes of extragenic suppressors-two translation initiation factors and a ribosomal protein-all of which are involved in translation initiation. The translation rate and inner membrane proteomic analyses were combined to define the mechanism that compensates for the lack of SRP. The primary factor that contributes to the efficiency of IMP targeting is the extension of the time window for targeting by pausing the initiation of translation, which further reduces translation initiation and elongation rates. Furthermore, we found that easily predictable features in the nascent chain determine the specificity of protein targeting. Our results show why the loss of the SRP pathway does not lead to lethality. We report a new paradigm in which the time delay in translation initiation is beneficial during protein targeting in the absence of SRP.IMPORTANCE Inner membrane proteins (IMPs) are cotranslationally inserted into the inner membrane or endoplasmic reticulum by the signal recognition particle (SRP). Generally, the deletion of SRP can result in protein targeting defects in Escherichia coli Suppressor screening for loss of SRP reveals that pausing at the translation start site is likely to be critical in allowing IMP targeting and avoiding aggregation. In this work, we found for the first time that SRP is nonessential in E. coli The time delay in initiation is different from the previous mechanism that only slows down the elongation rate. It not only maximizes the opportunity for untranslated ribosomes to be near the inner membrane but also extends the time window for targeting translating ribosomes by decreasing the speed of translation. We anticipate that our work will be a starting point for a more delicate regulatory mechanism of protein targeting.

Genomic, transcriptomic, and metabolic characterizations of Escherichia coli adapted to branched-chain higher alcohol tolerance.

Microbial-produced branched-chain higher alcohols (BCHAs), such as isopropanol, isobutanol, and isopentanol in Escherichia coli, have emerged as promising alternative biofuels under development. Elucidating and improving the tolerance of E. coli to BCHAs are important issues for microbial production of BCHAs due to their physiological inhibitory effect. Previous works aimed at understanding the genetic basis of E. coli tolerance to BCHAs with a comparative genome, reverse engineering, or transcriptome approach have gained some important insights into the mechanism of tolerance. However, investigation on BCHA tolerance from the whole-genomic, transcriptomic, and metabolic levels via a systematic approach has not yet been completely elucidated. Here, in this study, genomic, transcriptomic, and 13C-metabolic flux analyses (13C-MFA) of an evolved E. coli strain adapted to BCHA tolerance were conducted. Genome mutation of negative regulation factor (rssB, acrB, and clpX) of RpoS level suggested upregulation of RpoS activity in BCHA tolerance of E. coli. From a more detailed perspective, enhanced energy metabolism was observed to be the main characteristic of E. coli strain tolerant to BCHAs. Enhanced energy metabolism has been achieved through several routes, which included redistribution of the central carbon metabolism, upregulation of the energy generation machinery, and facilitating the operation of electron transferring chain. Evidence of multiple solutions of genotype modification toward BCHA tolerance was also revealed through comparative analysis of previous works from different groups.

Find_tfSBP: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks.

Biologically meaningful metabolic pathways are important references in the design of industrial bacterium. At present, constraint-based method is the only way to model and simulate a genome-scale metabolic network under steady-state criteria. Due to the inadequate assumption of the relationship in gene-enzyme-reaction as one-to-one unique association, computational difficulty or ignoring the yield from substrate to product, previous pathway finding approaches can't be effectively applied to find out the high yield pathways that are mass balanced in stoichiometry. In addition, the shortest pathways may not be the pathways with high yield. At the same time, a pathway, which exists in stoichiometry, may not be feasible in thermodynamics. By using mixed integer programming strategy, we put forward an algorithm to identify all the smallest balanced pathways which convert the source compound to the target compound in large-scale metabolic networks. The resulting pathways by our method can finely satisfy the stoichiometric constraints and non-decomposability condition. Especially, the functions of high yield and thermodynamics feasibility have been considered in our approach. This tool is tailored to direct the metabolic engineering practice to enlarge the metabolic potentials of industrial strains by integrating the extensive metabolic network information built from systems biology dataset.

Profiling of Saccharomyces cerevisiae transcription factors for engineering the resistance of yeast to lignocellulose-derived inhibitors in biomass conversion.

BACKGROUND: Yeast transcription factors (TFs) involved in the regulation of multidrug resistance (MDR) were investigated in experiments with deletion mutants, transformants overexpressing synthetic genes encoding TFs, and toxic concentrations of lignocellulose-derived substances added to cultures as complex mixtures or as specific compounds, viz. coniferyl aldehyde, 5-hydroxymethylfurfural, and furfural. RESULTS: In the presence of complex mixtures of toxic substances from spruce wood, transformants overexpressing YAP1 and STB5, TFs involved in oxidative stress response, exhibited enhanced relative growth rates amounting to 4.589 ± 0.261 and 1.455 ± 0.185, respectively. Other TFs identified as important for resistance included DAL81, GZF3, LEU3, PUT3, and WAR1. Potential overlapping functions of YAP1 and STB5 were investigated in experiments with permutations of deletions and overexpression of the two genes. YAP1 complemented STB5 with respect to resistance to 5-hydroxymethylfurfural, but had a distinct role with regard to resistance to coniferyl aldehyde as deletion of YAP1 rendered the cell incapable of resisting coniferyl aldehyde even if STB5 was overexpressed. CONCLUSIONS: We have investigated 30 deletion mutants and eight transformants overexpressing MDR transcription factors with regard to the roles the transcription factors play in the resistance to toxic concentrations of lignocellulose-derived substances. This work provides an overview of the involvement of thirty transcription factors in the resistance to lignocellulose-derived substances, shows distinct and complementary roles played by YAP1 and STB5, and offers directions for the engineering of robust yeast strains for fermentation processes based on lignocellulosic feedstocks.

High-throughput metagenomic analysis of petroleum-contaminated soil microbiome reveals the versatility in xenobiotic aromatics metabolism.

The soil with petroleum contamination is one of the most studied soil ecosystems due to its rich microorganisms for hydrocarbon degradation and broad applications in bioremediation. However, our understanding of the genomic properties and functional traits of the soil microbiome is limited. In this study, we used high-throughput metagenomic sequencing to comprehensively study the microbial community from petroleum-contaminated soils near Tianjin Dagang oilfield in eastern China. The analysis reveals that the soil metagenome is characterized by high level of community diversity and metabolic versatility. The metageome community is predominated by γ-Proteobacteria and α-Proteobacteria, which are key players for petroleum hydrocarbon degradation. The functional study demonstrates over-represented enzyme groups and pathways involved in degradation of a broad set of xenobiotic aromatic compounds, including toluene, xylene, chlorobenzoate, aminobenzoate, DDT, methylnaphthalene, and bisphenol. A composite metabolic network is proposed for the identified pathways, thus consolidating our identification of the pathways. The overall data demonstrated the great potential of the studied soil microbiome in the xenobiotic aromatics degradation. The results not only establish a rich reservoir for novel enzyme discovery but also provide putative applications in bioremediation.

ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network.

Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP) based on metabolic networks, and use duality theory to transform bi-level optimization problem of large-scale MIBLP to single-level programming. However, the validity of the transformation was not proved. Solution of MIBLP depends on the structure of inner problem. If the inner problem is continuous, Karush-Kuhn-Tucker (KKT) method can be used to reformulate the MIBLP to a single-level one. We adopt KKT technique in our algorithm ReacKnock to attack the intractable problem of the solution of MIBLP, demonstrated with the genome-scale metabolic network model of E. coli for producing various chemicals such as succinate, ethanol, threonine and etc. Compared to the previous methods, our algorithm is fast, stable and reliable to find the optimal solutions for all the chemical products tested, and able to provide all the alternative deletion strategies which lead to the same industrial objective.

Analysis of enhanced current-generating mechanism of Geobacter sulfurreducens strain via model-driven metabolism simulation.

Microbial fuel cells (MFCs) are a class of ideal technologies that function via anaerobic respiration of electricigens, which bring current generation and environmental restoration together. An in-depth understanding of microbial metabolism is of great importance in engineering microbes to further improve their respiration. We employed flux balance analysis and selected Fe(iii) as a substitute for the electrode to simulate current-generating metabolism of Geobacter sulfurreducens PCA with a fixed acetate uptake rate. Simulation results indicated the fluxes of reactions directing acetate towards dissimilation to generate electrons increased under the suboptimal growth condition, resulting in an increase in the respiration rate and a decrease in the growth rate. The results revealed the competitive relationship between oxidative respiration and cell growth during the metabolism of microbe current generation. The results helped us quantitatively understand why microbes growing slowly have the potential to make good use of fuel in MFCs. At the same time, slow growth does not necessarily result in speedy respiration. Alternative respirations may exist under the same growth state due to redundant pathways in the metabolic network. The big difference between the maximum and minimum respiration mainly results from the total formate secretion. With iterative flux variability analysis, a relatively ideal model of variant of G. sulfurreducens PCA was reconstructed by deleting several enzymes in the wild model, which could reach simultaneous suboptimal growth and maximum respiration. Under this ideal condition, flux towards extracellular electron transfer rather than for biosynthesis is beneficial for the conversion of organic matter to electricity without large accumulations of biomass and electricigens may maximize utilization of limited fuel. Our simulations will provide an insight into the enhanced current-generating mechanism and identify theoretical range of respiration rates for guiding strain improvement in MFCs.

Construction and analysis of the model of energy metabolism in E. coli.

Genome-scale models of metabolism have only been analyzed with the constraint-based modelling philosophy and there have been several genome-scale gene-protein-reaction models. But research on the modelling for energy metabolism of organisms just began in recent years and research on metabolic weighted complex network are rare in literature. We have made three research based on the complete model of E. coli's energy metabolism. We first constructed a metabolic weighted network using the rates of free energy consumption within metabolic reactions as the weights. We then analyzed some structural characters of the metabolic weighted network that we constructed. We found that the distribution of the weight values was uneven, that most of the weight values were zero while reactions with abstract large weight values were rare and that the relationship between w (weight values) and v (flux values) was not of linear correlation. At last, we have done some research on the equilibrium of free energy for the energy metabolism system of E. coli. We found that E(out) (free energy rate input from the environment) can meet the demand of E(ch)(in) (free energy rate dissipated by chemical process) and that chemical process plays a great role in the dissipation of free energy in cells. By these research and to a certain extend, we can understand more about the energy metabolism of E. coli.

Development of thermodynamic optimum searching (TOS) to improve the prediction accuracy of flux balance analysis.

Flux balance analysis (FBA) has been widely used in calculating steady-state flux distributions that provide important information for metabolic engineering. Several thermodynamics-based methods, for example, quantitative assignment of reaction directionality and energy balance analysis have been developed to improve the prediction accuracy of FBA. However, these methods can only generate a thermodynamically feasible range, rather than the most thermodynamically favorable solution. We therefore developed a novel optimization method termed as thermodynamic optimum searching (TOS) to calculate the thermodynamically optimal solution, based on the second law of thermodynamics, the minimum magnitude of the Gibbs free energy change and the maximum entropy production principle (MEPP). Then, TOS was applied to five physiological conditions of Escherichia coli to evaluate its effectiveness. The resulting prediction accuracy was found significantly improved (10.7-48.5%) by comparing with the (13)C-fluxome data, indicating that TOS can be considered an advanced calculation and prediction tool in metabolic engineering.

Genome-scale analysis to the impact of gene deletion on the metabolism of E. coli: constraint-based simulation approach.

BACKGROUND: Genome-scale models of metabolism have only been analyzed with the constraint-based modelling philosophy. Some gene deletion studies on in silico organism models at genome-scale have been made, but most of them were from the aspects of distinguishing lethal and non-lethal genes or growth rate. The impact of gene deletion on flux redistribution, the functions and characters of key genes, and the performance of different reactions in entire gene deletion still lack research. RESULTS: Three main researches have been done into the metabolism of E. coli in gene deletion. The first work was about finding key genes and subsystems: First, by calculating the deletion impact p of whole 1261 genes, one by one, on the metabolic flux redistribution of E. coli_iAF1260, we can find that p is more detailed in describing the change of organism's metabolism. Next, we sought out 195 important (high-p) genes, and they are more than essential genes (growth rate f becomes zero if deleting). So we speculated that under some circumstances and when an important gene is deleted, a big change in the metabolic system of E. coli has taken place and E. coli may use other reaction ways to strive to live. Further, by determining the functional subsystems to which 195 key genes belong, we found that their distribution to subsystems was not even and most of them were related to just three subsystems and that all of the 8 important but not essential genes appear just in "Oxidative Phosphorylation". Our second work was about p's three characters: We analyzed the correlation between p and d (connection degree of one gene) and the correlation between p and vgene (flux sum controlled by one gene), and found that both of them are not of linear correlation, but the correlation between p and f is of highly linear correlation. The third work was about highly-affected reactions: We found 16 reactions with more than 2000 Rg value (measuring the impact that a reaction is gotten in the whole 1261 gene deletion). We speculated that highly-affected reactions involve in the metabolism of basic biomasses. CONCLUSION: To sum up, these results we obtained have biological significances and our researches will shed new light on the future researches.