Improving the production of carbamoyltobramycin by an industrial Streptoalloteichus tenebrarius through metabolic engineering.

Tobramycin is an essential and extensively used broad-spectrum aminoglycoside antibiotic obtained through alkaline hydrolysis of carbamoyltobramycin, one of the fermentation products of Streptoalloteichus tenebrarius. To simplify the composition of fermentation products from industrial strain, the main byproduct apramycin was blocked by gene disruption and constructed a mutant mainly producing carbamoyltobramycin. The generation of antibiotics is significantly affected by the secondary metabolism of actinomycetes which could be controlled by modifying the pathway-specific regulatory proteins within the cluster. Within the tobramycin biosynthesis cluster, a transcriptional regulatory factor TobR belonging to the Lrp/AsnC family was identified. Based on the sequence and structural characteristics, tobR might encode a pathway-specific transcriptional regulatory factor during biosynthesis. Knockout and overexpression strains of tobR were constructed to investigate its role in carbamoyltobramycin production. Results showed that knockout of TobR increased carbamoyltobramycin biosynthesis by 22.35%, whereas its overexpression decreased carbamoyltobramycin production by 10.23%. In vitro electrophoretic mobility shift assay (EMSA) experiments confirmed that TobR interacts with DNA at the adjacent tobO promoter position. Strains overexpressing tobO with ermEp* promoter exhibited 36.36% increase, and tobO with kasOp* promoter exhibited 22.84% increase in carbamoyltobramycin titer. When the overexpressing of tobO and the knockout of tobR were combined, the production of carbamoyltobramycin was further enhanced. In the shake-flask fermentation, the titer reached 3.76 g/L, which was 42.42% higher than that of starting strain. Understanding the role of Lrp/AsnC family transcription regulators would be useful for other antibiotic biosynthesis in other actinomycetes. KEY POINTS: • The transcriptional regulator TobR belonging to the Lrp/AsnC family was identified.  • An oxygenase TobO was identified within the tobramycin biosynthesis cluster. • TobO and TobR have significant effects on the synthesis of carbamoyltobramycin.

The Recognition Pathway of the SARS-CoV-2 Spike Receptor-Binding Domain to Human Angiotensin-Converting Enzyme 2.

COVID-19 caused by SARS-CoV-2 has spread around the world. The receptor-binding domain (RBD) of the spike protein of SARS-CoV-2 is a critical component that directly interacts with host ACE2. Here, we simulate the ACE2 recognition processes of RBD of the WT, Delta, and OmicronBA.2 variants using our recently developed supervised Gaussian accelerated molecular dynamics (Su-GaMD) approach. We show that RBD recognizes ACE2 through three contact regions (regions I, II, and III), which aligns well with the anchor-locker mechanism. The higher binding free energy in State d of the RBDOmicronBA.2-ACE2 system correlates well with the increased infectivity of OmicronBA.2 in comparison with other variants. For RBDDelta, the T478K mutation affects the first step of recognition, while the L452R mutation, through its nearby Y449, affects the RBDDelta-ACE2 binding in the last step of recognition. For RBDOmicronBA.2, the E484A mutation affects the first step of recognition, the Q493R, N501Y, and Y505H mutations affect the binding free energy in the last step of recognition, mutations in the contact regions affect the recognition directly, and other mutations indirectly affect recognition through dynamic correlations with the contact regions. These results provide theoretical insights for RBD-ACE2 recognition and may facilitate drug design against SARS-CoV-2.

Using the i-PARIHS theoretical framework to develop evidence implementation strategies for central venous catheter maintenance: a multi-site quality improvement project.

INTRODUCTION: Evidence-based nursing practice can reduce complications associated with central venous catheters (CVCs). In this project, the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework was considered an ideal theoretical instrument to identify facilitators and barriers to implementing evidence-based practice. METHODS: The project was conducted in pediatric intensive care units in six Chinese tertiary children's hospitals. Twenty-two audit criteria were obtained from best practice recommendations, and a baseline audit was conducted to assess current practice against best practice. Next, the i-PARIHS framework was used to identify facilitators and barriers to best practice and develop improvement strategies. A follow-up audit was then conducted to measure changes in compliance with best practices. RESULTS: Facilitators and barriers were identified at the innovation, recipient, and context levels. A comprehensive CVC maintenance strategy was then developed to apply the best evidence to nurses' clinical work. Of the 22 audit criteria, 17 showed significant improvement compared with the baseline audit. CONCLUSIONS: The i-PARIHS framework is an effective tool for developing targeted, evidence-based improvement strategies and applying these to the clinical setting. The quality of the nurses' clinical practice improved during CVC maintenance. However, there is no certainty that these positive results can be maintained, and long-term data are needed to verify this. SPANISH ABSTRACT: http://links.lww.com/IJEBH/A185.

XylR Overexpression in Escherichia coli Alleviated Transcriptional Repression by Arabinose and Enhanced Xylitol Bioproduction from Xylose Mother Liquor.

Xylitol is a polyol widely used in food, pharmaceuticals, and light industries. It is currently produced through the chemical catalytic hydrogenation of xylose and generates xylose mother liquor as a substantial byproduct in the procedure of xylose extraction. If xylose mother liquor could also be efficiently bioconverted to xylitol, the greenness and atom economy of xylitol production would be largely improved. However, xylose mother liquor contains a mixture of glucose, xylose, and arabinose, raising the issue of carbon catabolic repression in its utilization by microbial conversion. Targeting this challenge, the transcriptional activator XylR was overexpressed in a previously constructed xylitol-producing E. coli strain CPH. The resulting strain CPHR produced 16.61 g/L of xylitol in shake-flask cultures from the mixture of corn cob hydrolysate and xylose mother liquor (1:1, v/v) with a xylose conversion rate of 90.1%, which were 2.23 and 2.15 times higher than the starting strain, respectively. Furthermore, XylR overexpression upregulated the expression levels of xylE, xylF, xylG, and xylH genes by 2.08-2.72 times in arabinose-containing medium, suggesting the alleviation of transcriptional repression of xylose transport genes by arabinose. This work lays the foundation for xylitol bioproduction from xylose mother liquor.

Tf2O-Mediated Tandem Reaction of Enaminones for the Synthesis of Functionalized Conjugated-Enals/ß-Naphthalaldehydes.

A highly efficient and regioselective method for constructing functionalized conjugated enals via the Tf2O-mediated tandem reaction of enaminones with thiophenols has been described. Chain products with excellent stereoselectivity could be obtained through substrate regulation. Additionally, a feasible method for synthesizing ß-naphthalaldehydes through PhSO2Na/DABCO promoting hydrogen atom transfer process has also been reported here. Mechanism studies have shown that 2-formyl vinyl triflate 8 and sulfonylated enal 9 were the key intermediates in this process.

Cross-sensitivity analysis of land use transition and ecological service values in rare earth mining areas in southern China.

Exploring the cross-sensitivity between land use transformation and ecological service values in rare earth mining areas is of great significance for the development of ecological protection and restoration in rare earth mining areas. To study the impact of land use changes on ecosystem service functions in rare earth mining areas, firstly, the land use change trends in the study area from 2009 to 2019 were analyzed using the land transfer matrix; then the distribution of ecosystem service values and the flow direction of ecosystem service values in the study area were measured based on the ecosystem service value equivalents; a spatial autocorrelation analysis was done on the ecosystem service values to explore their spatial distribution patterns; and finally, the cross-sensitivity coefficient was used to quantitatively assess the extent and direction of the impact of land use change on ecosystem service values. The results show that the land use types in the study area are mainly forest land and farmland, with woodland accounting for the highest proportion of the study area. The ESV changes in the study area are consistent with the trend of land use transformation, with the overall increase and decrease being comparable, and the decrease in ESV is mainly concentrated in the areas with a large increase in mining land and construction land; during the study period, the study area was significantly reduced with low-low cluster areas and the ecological environment was improved; from 2009 to 2014, the ecological sensitivity coefficient is more variable, and is more sensitive to the net conversion between water and desert, from 2014 to 2019, the ecological sensitivity coefficient is less variable, and the most sensitive is the net conversion between cultivated land and water. The study area should be reasonably developed for rare earth resources and the ecological environment around the mining area should be reasonably protected to build an ecological security pattern.

An Energetic Approach to Task-Invariant Ankle Exoskeleton Control.

Robotic ankle exoskeletons have been shown to reduce human effort during walking. However, existing ankle exoskeleton control approaches are limited in their ability to apply biomimetic torque across diverse tasks outside of the controlled lab environment. Energy shaping control can provide task-invariant assistance without estimating the user's state, classifying task, or reproducing pre-defined torque trajectories. In previous work, we showed that an optimally task-invariant energy shaping controller implemented on a knee-ankle exoskeleton reduced the effort of certain muscles for a range of tasks. In this paper, we extend this approach to the sensor suite available at the ankle and present its implementation on a commercially-available, bilateral ankle exoskeleton. An experiment with three healthy subjects walking on a circuit and on a treadmill showed that the controller can approximate biomimetic profiles for varying terrains and task transitions without classifying tasks or switching control modes.

Trends and hotspots in research on medical images with deep learning: a bibliometric analysis from 2013 to 2023.

Background: With the rapid development of the internet, the improvement of computer capabilities, and the continuous advancement of algorithms, deep learning has developed rapidly in recent years and has been widely applied in many fields. Previous studies have shown that deep learning has an excellent performance in image processing, and deep learning-based medical image processing may help solve the difficulties faced by traditional medical image processing. This technology has attracted the attention of many scholars in the fields of computer science and medicine. This study mainly summarizes the knowledge structure of deep learning-based medical image processing research through bibliometric analysis and explores the research hotspots and possible development trends in this field. Methods: Retrieve the Web of Science Core Collection database using the search terms "deep learning," "medical image processing," and their synonyms. Use CiteSpace for visual analysis of authors, institutions, countries, keywords, co-cited references, co-cited authors, and co-cited journals. Results: The analysis was conducted on 562 highly cited papers retrieved from the database. The trend chart of the annual publication volume shows an upward trend. Pheng-Ann Heng, Hao Chen, and Klaus Hermann Maier-Hein are among the active authors in this field. Chinese Academy of Sciences has the highest number of publications, while the institution with the highest centrality is Stanford University. The United States has the highest number of publications, followed by China. The most frequent keyword is "Deep Learning," and the highest centrality keyword is "Algorithm." The most cited author is Kaiming He, and the author with the highest centrality is Yoshua Bengio. Conclusion: The application of deep learning in medical image processing is becoming increasingly common, and there are many active authors, institutions, and countries in this field. Current research in medical image processing mainly focuses on deep learning, convolutional neural networks, classification, diagnosis, segmentation, image, algorithm, and artificial intelligence. The research focus and trends are gradually shifting toward more complex and systematic directions, and deep learning technology will continue to play an important role.

Multisite Mutation of the Escherichia coli cAMP Receptor Protein: Enhancing Xylitol Biosynthesis by Activating Xylose Catabolism and Improving Strain Tolerance.

The bioproduction of xylitol from hemicellulose hydrolysate has good potential for industrial development. However, xylitol productivity has always been limited due to corncob hydrolysate toxicity and glucose catabolic repression. To address these challenges, this work selected the S83 and S128 amino acid residues of the cyclic AMP receptor protein (CRP) as the modification target. By introducing multisite mutation in CRP, this approach successfully enhanced xylose catabolism and improved the strain's tolerance to corncob hydrolysate. The resulting mutant strain, designated as CPH (CRP S83H-S128P), underwent fermentation in a 20 L bioreactor with semicontinuous feeding of corncob hydrolysate. Remarkably, xylitol yield and xylitol productivity for 41 h fermentation were 175 and 4.32 g/L/h, respectively. Therefore, multisite CRP mutation was demonstrated as an efficient global regulatory strategy to effectively improve xylitol productivity from lime-pretreated corncob hydrolysates.

Optimal Energy Shaping Control for a Backdrivable Hip Exoskeleton.

Task-dependent controllers widely used in exoskeletons track predefined trajectories, which overly constrain the volitional motion of individuals with remnant voluntary mobility. Energy shaping, on the other hand, provides task-invariant assistance by altering the human body's dynamic characteristics in the closed loop. While human-exoskeleton systems are often modeled using Euler-Lagrange equations, in our previous work we modeled the system as a port-controlled-Hamiltonian system, and a task-invariant controller was designed for a knee-ankle exoskeleton using interconnection-damping assignment passivity-based control. In this paper, we extend this framework to design a controller for a backdrivable hip exoskeleton to assist multiple tasks. A set of basis functions that contains information of kinematics is selected and corresponding coefficients are optimized, which allows the controller to provide torque that fits normative human torque for different activities of daily life. Human-subject experiments with two able-bodied subjects demonstrated the controller's capability to reduce muscle effort across different tasks.

Risk factors for neck pain in college students: a systematic review and meta-analysis.

BACKGROUND: During the COVID-19 epidemic, the prevalence of neck pain among college students has increased due to the shift from offline to online learning and increasing academic and employment pressures. Therefore, this systematic review aimed to identify the personal, occupational, and psychological factors associated with the development of neck pain to promote the development of preventive strategies and early intervention treatment. METHODS: Seven electronic databases were searched from inception to December 2022 for cross-sectional studies, cohort studies, case----control studies, and randomized controlled trials (RCTs) on neck pain. The quality of the selected studies were assessed by American Agency for Healthcare Research and Quality (AHRQ) or the Newcastle-Ottawa Scale (NOS). Pooled odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated to evaluate the effects of the included risk factors on neck pain. RESULTS: Thirty studies were included, including 18,395 participants. And a total of 33 potentially associated risk factors were identified. Ultimately, 11 risk factors were included in the meta-analysis after assessing, and all results were statistically significant (P < 0.05). The factors supported by strong evidence mainly include the improper use of the pillow (OR = 2.20, 95% CI: 1.39 to 3.48), lack of exercise (OR = 1.88, 95% CI: 1.53 to 2.30), improper sitting posture (OR = 1.97, 95% CI: 1.39 to 2.78), history of neck and shoulder trauma (OR = 2.32, 95% CI: 1.79 to 3.01), senior grade (OR = 2.86, 95% CI: 2.07 to 3.95), staying up late (OR = 1.80, 95% CI: 1.35 to 2.41), long-time electronic product usage daily (OR = 1.53, 95% CI: 1.33 to 1.76), long-time to bow head (OR = 2.04, 95% CI: 1.58 to 2.64), and emotional problems (OR = 2.09; 95% CI: 1.66  to 2.63). Risk factors supported by moderate evidence were high stress (OR = 1.61, 95% CI: 1.02 to 2.52) and female gender (OR = 1.69, 95% CI: 1.52 to 1.87). CONCLUSION: This study obtained 11 main risk factors affecting college students neck pain, including improper use of the pillow, lack of exercise, improper sitting posture, history of neck and shoulder trauma, senior grade, staying up late, long-term electronic product usage daily, long time to bow head, high stress, emotional problems and female gender.

Machine Learning Enables Prediction of Pyrrolysyl-tRNA Synthetase Substrate Specificity.

Knowledge about the substrate scope for a given enzyme is informative for elucidating biochemical pathways and also for expanding applications of the enzyme. However, no general methods are available to accurately predict the substrate specificity of an enzyme. Pyrrolysyl-tRNA synthetase (PylRS) is a powerful tool for incorporating various noncanonical amino acids (NCAAs) into proteins, which enabled us to probe, image, rationally engineer, and evolve protein structure and function. However, the incorporation of a new NCAA typically requires the selection of large libraries of PylRS with randomized mutations at active sites, and this process requires multiple rounds of selection for each new substrate. Therefore, a single aminoacyl-tRNA synthetase with broad substrate promiscuity is ideal to facilitate widespread applications of the genetic NCAA incorporation technique. Herein, machine learning models were developed to predict the substrate specificity of PylRS to accept novel NCAAs that could be incorporated into proteins by three PylRS mutants. The models were built from a training set of 285 unique enzyme-substrate pairs of three PylRS mutants including IFRS, BtaRS, and MFRS against 95 NCAAs. The best BaggingTree (BT) model was then used for virtually screening a NCAAs library containing 1474 phenylalanine, tyrosine, tryptophan, and alanine analogues, and 156 NCAAs were predicted to be accepted by at least one of the three PylRS mutants. Then, 27 NCAAs including 24 positive and 3 negative substrates were experimentally tested for their activities, and 20 of the 24 positive substrates showed weak or strong activity and were accepted by at least one PylRS mutant, among which 11 NCAAs were never reported to be incorporated into proteins before. Three negative substrates did not show any activity. Experimental results suggested that the BT model provides a three-class classification accuracy of 0.69 and a binary classification accuracy of 0.86. This study expanded the substrate scope of three PylRS variants and provided a framework for developing machine learning models to predict substrate specificity of other PylRS variants.

Verbenalin alleviates acute lung injury induced by sepsis and IgG immune complex through GPR18 receptor.

Acute lung injury is significantly associated with the aberrant activation and pyroptosis of alveolar macrophages. Targeting the GPR18 receptor presents a potential therapeutic approach to mitigate inflammation. Verbenalin, a prominent component of Verbena in Xuanfeibaidu (XFBD) granules, is recommended for treating COVID-19. In this study, we demonstrate the therapeutic effect of verbenalin on lung injury through direct binding to the GPR18 receptor. Verbenalin inhibits the activation of inflammatory signaling pathways induced by lipopolysaccharide (LPS) and IgG immune complex (IgG IC) via GPR18 receptor activation. The structural basis for verbenalin's effect on GPR18 activation is elucidated through molecular docking and molecular dynamics simulations. Furthermore, we establish that IgG IC induces macrophage pyroptosis by upregulating the expression of GSDME and GSDMD through CEBP-δ activation, while verbenalin inhibits this process. Additionally, we provide the first evidence that IgG IC promotes the formation of neutrophil extracellular traps (NETs), and verbenalin suppresses NETs formation. Collectively, our findings indicate that verbenalin functions as a "phytoresolvin" to promote inflammation regression and suggests that targeting the C/EBP-δ/GSDMD/GSDME axis to inhibit macrophage pyroptosis may represent a novel strategy for treating acute lung injury and sepsis.

Creation of a Prognostic Model Using Cuproptosis-Associated Long Noncoding RNAs in Hepatocellular Carcinoma.

Cuproptosis is an unusual form of cell death caused by copper accumulation in mitochondria. Cuproptosis is associated with hepatocellular carcinoma (HCC). Long noncoding RNAs (LncRNAs) have been shown to be effective prognostic biomarkers, yet the link between lncRNAs and cuproptosis remains unclear. We aimed to build a prognostic model of lncRNA risk and explore potential biomarkers of cuproptosis in HCC. Pearson correlations were used to derive lncRNAs co-expressed in cuproptosis. The model was constructed using Cox, Lasso, and multivariate Cox regressions. Kaplan-Meier survival analysis, principal components analysis, receiver operating characteristic curve, and nomogram analyses were carried out for validation. Seven lncRNAs were identified as prognostic factors. A risk model was an independent prognostic predictor. Among these seven lncRNAs, prostate cancer associated transcript 6 (PCAT6) is highly expressed in different types of cancer, activating Wnt, PI3K/Akt/mTOR, and other pathways; therefore, we performed further functional validation of PCAT6 in HCC. Reverse transcription-polymerase chain reaction results showed that PCAT6 was aberrantly highly expressed in HCC cell lines (HepG2 and Hep3B) compared to LO2 (normal hepatocytes). When its expression was knocked down, cells proliferated and migrated less. PCAT6 might be a potential biomarker for predicting prognosis in HCC.

Significance of cuproptosis-related lncRNA signature in LUAD prognosis and immunotherapy: A machine learning approach.

OBJECTIVE: Cuproptosis-related genes are closely related to lung adenocarcinoma (LUAD), which can be analyzed via the analysis of long noncoding RNA (lncRNA). To date, the clinical significance and function of cuproptosis-related lncRNAs are still not well elucidated. Further analysis of cuproptosis-related prognostic lncRNAs is of great significance for the treatment, diagnosis, and prognosis of LUAD. METHODS: In this study, a multiple machine learning (ML)-based computational approach was proposed for the identification of the cuproptosis-related lncRNAs signature (CRlncSig) via comprehensive analysis of cuproptosis, lncRNAs, and clinical characteristics. The proposed approach integrated multiple ML algorithms (least absolute shrinkage and selection operator regression analysis, univariate and multivariate Cox regression) to effectively identify the CRlncSig. RESULTS: Based on the proposed approach, the CRlncSig was identified from the 3450 cuproptosis-related lncRNAs, which consist of 13 lncRNAs (CDKN2A-DT, FAM66C, FAM83A-AS1, AL359232.1, FRMD6-AS1, AC027237.4, AC023090.1, AL157888.1, AL627443.3, AC026355.2, AC008957.1, AP000346.1, and GLIS2-AS1). CONCLUSIONS: The CRlncSig could well predict the prognosis of different LUAD patients, which is different from other clinical features. Moreover, the CRlncSig was proved to be an effective indicator of patient survival via functional characterization analysis, which is relevant to cancer progression and immune infiltration. Furthermore, the results of RT-PCR assay indicated that the expression level of FAM83A-AS1 and AC026355.2 in A549 and H1975 cells (LUAD) was significantly higher than that in BEAS-2B cells (normal lung epithelial).

Molecular Understanding of the Interfacial Interaction and Corrosion Resistance between Epoxy Adhesive and Metallic Oxides on Galvanized Steel.

The epoxy adhesive-galvanized steel adhesive structure has been widely used in various industrial fields, but achieving high bonding strength and corrosion resistance is a challenge. This study examined the impact of surface oxides on the interfacial bonding performance of two types of galvanized steel with Zn-Al or Zn-Al-Mg coatings. Scanning electron microscopy and X-ray photoelectron spectroscopy analysis showed that the Zn-Al coating was covered by ZnO and Al2O3, while MgO was additionally found on the Zn-Al-Mg coating. Both coatings exhibited excellent adhesion in dry environments, but after 21 days of water soaking, the Zn-Al-Mg joint demonstrated better corrosion resistance than the Zn-Al joint. Numerical simulations revealed that metallic oxides of ZnO, Al2O3, and MgO had different adsorption preferences for the main components of the adhesive. The adhesion stress at the coating-adhesive interface was mainly due to hydrogen bonds and ionic interactions, and the theoretical adhesion stress of MgO adhesive system was higher than that of ZnO and Al2O3. The corrosion resistance of the Zn-Al-Mg adhesive interface was mainly due to the stronger corrosion resistance of the coating itself, and the lower water-related hydrogen bond content at the MgO adhesive interface. Understanding these bonding mechanisms can lead to the development of improved adhesive-galvanized steel structures with enhanced corrosion resistance.

Chinese medicinal herbs for reducing endocrine therapy-induced side effects in patients with hormone receptor-positive breast cancer: a systematic review and meta-analysis.

CONTEXT: Chinese medicinal herbs (CMH) have been considered a potentially efficacious approach for patients with breast cancer that experience adverse effects from endocrine treatment. OBJECTIVE: To investigate the impact of CMH on endocrine therapy-induced side effects in patients with hormone receptor-positive (HR+) breast cancer. METHODS: Ten databases (e.g., PubMed, Web of Science, Cochrane Library, China National Knowledge Information Database and other databases) were searched up to 20 May 2022. The search terms included Chinese herb, breast cancer, endocrine therapy, clinical trial and their mesh terms. The study selection and data extraction were performed by two independent reviewers. The risk of bias was evaluated using the Cochrane risk of bias method. RESULTS: A total of 31 studies with 2288 patients were included. There were significant improvements in bone mineral density (BMD) [lumbar BMD (MD 0.08, 95% CI 0.07 to 0.09, p < 0.00001) and femoral neck BMD (MD 0.08, 95% CI 0.07 to 0.10, p < 0.00001)] and bone gal protein (BGP) (MD 0.24, 95% CI 0.17 to 0.31, p < 0.00001), with a significant reduction in triglycerides (MD -0.53, 95% CI -1.00 to -0.07, p < 0.05) and no effect on estradiol levels (MD 0.90, 95% CI -0.31 to 2.12, p = 0.15). CONCLUSIONS: CMH combined with complementary therapy can moderately reduce endocrine therapy-induced side effects, including bone loss and dyslipidemia in patients with HR + breast cancer, revealing the potential role of CMH in treating (HR+) breast cancer. More high-quality RCTs are warranted to further validate the effectiveness and safety of CMH.

Learned Image Compression With Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules.

Recently deep learning-based image compression methods have achieved significant achievements and gradually outperformed traditional approaches including the latest standard Versatile Video Coding (VVC) in both PSNR and MS-SSIM metrics. Two key components of learned image compression are the entropy model of the latent representations and the encoding/decoding network architectures. Various models have been proposed, such as autoregressive, softmax, logistic mixture, Gaussian mixture, and Laplacian. Existing schemes only use one of these models. However, due to the vast diversity of images, it is not optimal to use one model for all images, even different regions within one image. In this paper, we propose a more flexible discretized Gaussian-Laplacian-Logistic mixture model (GLLMM) for the latent representations, which can adapt to different contents in different images and different regions of one image more accurately and efficiently, given the same complexity. Besides, in the encoding/decoding network design part, we propose a concatenated residual blocks (CRB), where multiple residual blocks are serially connected with additional shortcut connections. The CRB can improve the learning ability of the network, which can further improve the compression performance. Experimental results using the Kodak, Tecnick-100 and Tecnick-40 datasets show that the proposed scheme outperforms all the leading learning-based methods and existing compression standards including VVC intra coding (4:4:4 and 4:2:0) in terms of the PSNR and MS-SSIM. The source code is available at https://github.com/fengyurenpingsheng.

PRMT5 promotes ovarian cancer growth through enhancing Warburg effect by methylating ENO1.

Protein arginine methyltransferase 5 (PRMT5) is a major type II enzyme responsible for symmetric dimethylation of arginine (SDMA), and plays predominantly roles in human cancers, including in ovarian cancer. However, the exactly roles and underlying mechanisms of PRMT5 contributing to the progression of ovarian cancer mediated by reprogramming cell metabolism remain largely elusive. Here, we report that PRMT5 is highly expressed and correlates with poor survival in ovarian cancer. Knockdown or pharmaceutical inhibition of PRMT5 is sufficient to decrease glycolysis flux, attenuate tumor growth, and enhance the antitumor effect of Taxol. Mechanistically, we find that PRMT5 symmetrically dimethylates alpha-enolase (ENO1) at arginine 9 to promotes active ENO1 dimer formation, which increases glycolysis flux and accelerates tumor growth. Moreover, PRMT5 signals high glucose to increase the methylation modification of ENO1. Together, our data reveal a novel role of PRMT5 in promoting ovarian cancer growth by controlling glycolysis flux mediated by methylating ENO1, and highlights that PRMT5 may represent a promising therapeutic target for treating ovarian cancer.

Interface Wetting Driven by Laplace Pressure on Multiscale Topographies and Its Application to Performance Enhancement of Metal-Composite Hybrid Structure.

Surface topography reconstruction is extensively used to address the issue of weak bonding at the polymer-metal interface of metal-composite hybrid structure, while enhancement from this approach is seriously impaired by insufficient interface wetting. In this study, the wetting behavior of polymer on aluminum surfaces with multiscale topographies was theoretically and experimentally investigated to realize stable and complete wetting. Geometric dimensions of multiscale surface topographies have a notable impact on interfacial forces at the three-phase contact line of polymer/air/aluminum, and a competition exists between Laplace pressure and bubble pressure in dominating the wetting behavior. Laplace pressure facilitates the degassing of trapped air bubbles in grooves, bringing more robust interfacial wettability to grooves than dimples and grids. Conversely, dimples with excessive dimensions generate interfacial pores, and this intrinsic mechanism is theoretically unraveled. Moreover, different degrees of interface wetting cause variations in bonding strength of polymer-aluminum interface, which changes from ∼18% improvement to ∼17% reduction compared to original strength. Finally, groove topography perfectly achieved complete wetting between polymer and aluminum and consequently improved flexure performance by over 11% for the aluminum-carbon fiber hybrid side impact bar, which verifies the importance of complete wetting at a part scale. This study deepens the understanding of wetting behavior and clarifies the intrinsic correlation between interfacial bonding performance and surface topography.