Performance of the neural network-based prediction model in closed-loop adaptive optics.

Adaptive optics (AO) technology is an effective means to compensate for atmospheric turbulence, but the inherent delay error of an AO system will cause the compensation phase of the deformable mirror (DM) to lag behind the actual distortion, which limits the correction performance of the AO technology. Therefore, the feed-forward prediction of atmospheric turbulence has important research value and application significance to offset the inherent time delay and improve the correction bandwidth of the AO system. However, most prediction algorithms are limited to an open-loop system, and the deployment and the application in the actual AO system are rarely reported, so its correction performance improvement has not been verified in practice. We report, to our knowledge, the first successful test of a deep learning-based spatiotemporal prediction model in an actual 3 km laser atmospheric transport AO system and compare it with the traditional closed-loop control methods, demonstrating that the AO system with the prediction model has higher correction performance.

Unconventional Dually-Mobile Superrepellent Surfaces.

The ability of water droplets to move freely on superrepellent surfaces is a crucial feature that enables effective liquid repellency. Common superrepellent surfaces allow free motion of droplets in the Cassie state, with the liquid resting on the surface textures. However, liquid impalement into the textures generally leads to a wetting transition to the Wenzel state and droplet immobilization on the surface, thereby destroying the liquid repellency. In this study, we report the creation of a novel type of superrepellent surface through rational structural control combined with liquid-like surface chemistry, which allows for the free movement of water droplets and effective repellency in both the Cassie and Wenzel states. Theoretical guidelines for designing such surfaces are provided, and experimental results are consistent with theoretical analysis. Furthermore, we demonstrate the enhanced ice resistance of the dually-mobile superrepellent surfaces, along with their distinctive self-cleaning capability to eliminate internal contaminants. This study expands our understanding of superrepellency and offers new possibilities for the development of repellent surfaces with exceptional anti-wetting properties. This article is protected by copyright. All rights reserved.

Multi-type synchronization for coupled van der Pol oscillator systems with multiple coupling modes.

In this paper, we investigate synchronous solutions of coupled van der Pol oscillator systems with multiple coupling modes using the theory of rotating periodic solutions. Multiple coupling modes refer to two or three types of coupling modes in van der Pol oscillator networks, namely, position, velocity, and acceleration. Rotating periodic solutions can represent various types of synchronous solutions corresponding to different phase differences of coupled oscillators. When matrices representing the topology of different coupling modes have symmetry, the overall symmetry of the oscillator system depends on the intersection of the symmetries of the different topologies, determining the type of synchronous solutions for the coupled oscillator network. When matrices representing the topology of different coupling modes lack symmetry, if the adjacency matrices representing different coupling modes can be simplified into structurally identical quotient graphs (where weights can be proportional) through the same external equitable partition, the symmetry of the quotient graph determines the synchronization type of the original system. All these results are consistent with multi-layer networks where connections between different layers are one-to-one.

Screening of drug targets for tuberculosis on the basis of transcription factor regulatory network and mRNA sequencing technology.

Background: Tuberculosis is a worldwide epidemic disease, posing a serious threat to human health. To find effective drug action targets for Mycobacterium tuberculosis, differentially expressed genes in tuberculosis patients and healthy people were screened by mRNA sequencing in this study. A total of 556 differentially expressed genes in tuberculosis patients and healthy people were screened out by mRNA sequencing technology. 26 transcription factors and 66 corresponding target genes were screened out in the AnimalTFDB 3.0 database, and a transcription factor regulatory network was constructed. Results: Three key transcription factors (TP53, KLF5 and GATA2) and one key gene (AKT1) were screened as new potential drug targets and diagnostic targets for tuberculosis by MCODE cluster analysis, and the key genes and key transcription factors were verified by RT-PCR. Finally, we constructed the and a key factor and KEGG signaling pathway regulatory network to clarify the possible molecular pathogenesis of tuberculosis. Conclusion: This study suggested M. tuberculosis may activate the AKT1 gene expression by regulating transcription factors TP53, KLF5, and GATA2, thus activating the B cell receptor signaling pathway to induce the infection and invasion of M. tuberculosis. AKT1, TP53, KLF5, and GATA2 can be used as new potential drug targets for tuberculosis.

Knowledge and attitude of surgical patients and their families toward anesthesia.

Introduction: Anesthesia plays a critical role in modern surgical procedures by ensuring patient pain management and safety. This study aimed to investigate the knowledge and attitude of surgical patients and their families toward anesthesia. Methods: This prospective, cross-sectional study included patients and their families in Wenzhou, China. Data collection and the measurement of knowledge and attitude scores were administered using a self-administered questionnaire. Results: 503 participants (69.98% patients, 30.02% families) were included. The mean knowledge and attitude scores were 7.93 ± 6.11 (possible range: 0-26), and 32.64 ± 2.59 (possible range: 8-40), respectively, indicating an inadequate knowledge and positive attitude. Moreover, a multivariable logistic regression analysis showed that age [odd ratio (OR) = 0.394, p = 0.018], residence (OR = 0.424, p = 0.002), household income per month (OR = 0.297 ~ 0.380, p < 0.05), gender (OR = 1.680, p = 0.017), education (OR = 2.891, p = 0.017), and experienced anesthesia (OR = 4.405, p = 0.001) were independently associated with knowledge score. Additionally, knowledge score (OR = 1.096, p < 0.001), relationship with the patient (OR = 1.902, p = 0.009), and household income per month (OR = 0.545, p < 0.031) were independently associated with attitude score. Discussion: In conclusion, surgical patients and their families in Wenzhou, China had inadequate knowledge while positive attitude towards anesthesia, which might be influenced by their sociodemographic characteristics, including age, gender, residence, education, household income, relationship with patient, and experienced anesthesia. These findings emphasize the necessity of customized educational programs aimed at improving anesthesia knowledge and attitudes of patients and their families, especially among those with older age and lower socioeconomic status.

Technical Note: Swing golden angle - A navigator-interleaved golden angle trajectory with eddy current suppression - Application in free-running cardiac MRI.

BACKGROUND: Golden angle (GA) radial trajectory is advantageous for dynamic magnetic resonance imaging (MRI). Recently, several advanced algorithms have been developed based on navigator-interleaved GA trajectory to realize free-running cardiac MRI. However, navigator-interleaved GA trajectory suffers from the eddy-current effect, which reduces the image quality. PURPOSE: This work aims to integrate the navigator-interleaved GA trajectory with clinical cardiac MRI acquisition, with the minimum eddy-current artifacts. The ultimate goal is to realize a high-quality free-running cardiac imaging technique. METHODS: In this paper, we propose a new "swing golden angle" (swingGA) radial profile order. SwingGA samples the k-space by rotating back and forth at the generalized golden ratio interval, with smoothly interleaved navigator readouts. The sampling efficiency and angle increment distributions were investigated by numerical simulations. Static phantom imaging experiments were conducted to evaluate the eddy current effect, compared with cartesian, golden angle radial (GA), and tiny golden angle (tGA) trajectories. Furthermore, 12 heart-healthy subjects (aged 21-25 years) were recruited for free-running cardiac imaging with different sampling trajectories. Dynamic images were reconstructed by a low-rank subspace-constrained algorithm. The image quality was evaluated by signal-to-noise-ratio and spectrum analysis in the heart region, and compared with traditional clinical cardiac MRI images. RESULTS: SwingGA pattern achieves the highest sampling efficiency (mSE > 0.925) and the minimum azimuthal angle increment (mAD < 1.05). SwingGA can effectively suppress eddy currents in static phantom images, with the lowest normalized root mean square error (nRMSE) values among radial trajectories. For the in-vivo cardiac images, swingGA enjoys the highest SNR both in the blood pool and myocardium, and contains the minimum level of high-frequency artifacts. The free-running cardiac images have good consistency with traditional clinical cardiac MRI, and the swingGA sampling pattern achieves the best image quality among all sampling patterns. CONCLUSIONS: The proposed swingGA sampling pattern can effectively improve the sampling efficiency and reduce the eddy currents for the navigator-interleaved GA sequence. SwingGA is a promising sampling pattern for free-running cardiac MRI.

Type 2 diabetes attributable to ambient particulate matter pollution: a global burden study from 1990 to 2019.

Background: This study assesses the changes over time and geographical locations in the disease burden of type 2 diabetes (T2D) attributed to ambient particulate matter pollution (APMP) from 1990 to 2019 in 204 countries and regions with different socio-demographic indexes (SDI). Methods: The Global Burden of Diseases Study 2019 (GBD2019) database was used to analyze the global burden of T2D attributed to APMP. This study evaluated both the age-standardized death rate (ASDR) and disability-adjusted life years (DALYs) related to T2D, comparing data from 1990 to 2019. Estimated Annual Percentage Changes (EAPCs) were also utilized to investigate the trends over the 30-year study period. Results: The global age-standardized DALY rate and ASDR exhibited an increasing trend, with an EAPC of 2.21 (95% CI: 2.15 to 2.27) and 1.50 (95% CI: 1.43 to 1.58), respectively. This rise was most notable among older adult populations, men, regions in Africa and Asia, as well as low-middle SDI regions. In 2019, the ASDR for T2D caused by APMP was recorded at 2.47 per 100,000 population, while the DALY rate stood at 108.98 per 100,000 population. Males and countries with middle SDI levels displayed significantly high age-standardized death and DALY rates, particularly noticeable in Southern Sub-Saharan Africa. Conversely, regions with high SDI levels like High-income North America demonstrated decreasing trends. Conclusion: This study reveals a significant increase in T2D worldwide as a result of APMP from 1990 to 2019, with a particular emphasis on its impact on men, the older adult, and regions with low to middle SDI levels. These results underscore the urgent necessity for implementing policies aimed at addressing air pollution in order to reduce the prevalence of T2D, especially in the areas most heavily affected.

A Skin-Inspired High-Performance Tactile Sensor for Accurate Recognition of Object Softness.

High-performance tactile sensors with skin-sensing properties are crucial for intelligent perception in next-generation smart devices. However, previous studies have mainly focused on the sensitivity and response range of tactile sensation while neglecting the ability to recognize object softness. Therefore, achieving a precise perception of the softness remains a challenge. Here, we report an integrated tactile sensor consisting of a central hole gradient structure pressure sensor and a planar structure strain sensor. The recognition of softness and tactile perception is achieved through the synergistic effect of pressure sensors that sense the applied pressure and strain sensors that recognize the strain of the target object. The results indicate that the softness evaluation parameter (SC) of the integrated structural tactile sensor increases from 0.14 to 0.47 along with Young's modulus of the object decreasing from 2.74 to 0.45 MPa, demonstrating accurate softness recognition. It also exhibits a high sensitivity of 10.55 kPa-1 and an ultrawide linear range of 0-1000 kPa, showing an excellent tactile sensing capability. Further, an intelligent robotic hand system based on integrated structural tactile sensors was developed, which can identify the softness of soft foam and glass and grasp them accurately, indicating human skin-like sensing and grasping capabilities.

Atomic scale disorder and reconstruction in bulk infinite-layer nickelates lacking superconductivity.

The recent discovery of superconductivity in infinite-layer nickelate films has sparked significant interest and expanded the realm of superconductors, in which the infinite-layer structure and proper chemical doping are both of the essence. Nonetheless, the reasons for the absence of superconductivity in bulk infinite-layer nickelates remain puzzling. Herein, we investigate atomic defects and electronic structures in bulk infinite-layer Nd0.8Sr0.2NiO2 using scanning transmission electron microscopy. Our observations reveal the presence of three-dimensional (3D) block-like structural domains resulting from intersecting defect structures, disrupting the continuity within crystal grains, which could be a crucial factor in giving rise to the insulating character and inhibiting the emergence of superconductivity. Moreover, the infinite-layer structure, without complete topotactic reduction, retains interstitial oxygen atoms on the Nd atomic plane in bulk nickelates, possibly further aggravating the local distortions of NiO2 planes and hindering the superconductivity. These findings shed light on the existence of structural and atomic defects in bulk nickelates and provide valuable insights into the influence of proper topotactic reduction and structural orders on superconductivity.

Metabolome and Genome Analysis of a Novel Endophytic Fungus Aureobasidium pullulans KB3: Discovery of Polyketones and Polyketone Biosynthesis Pathway.

Endophytic fungi associated with plants may contain undiscovered bioactive compounds. Under standard laboratory conditions, most undiscovered compounds are inactive, whereas their production could be stimulated under different cultivation conditions. In this study, six endophytic fungi were isolated from the bark of Koelreuteria paniculata in Quancheng Park, Jinan City, Shandong Province, one of which was identified as a new subspecies of Aureobasidium pullulans, named A. pullulans KB3. Additionally, metabolomic tools were used to screen suitable media for A. pullulans KB3 fermentation, and the results showed that peptone dextrose medium (PDM) was more beneficial to culture A. pullulans KB3 for isolation of novel compounds. Sphaerolone, a polyketone compound, was initially isolated from A. pullulans KB3 via scaled up fermentation utilizing PDM. Additionally, the whole-genome DNA of A. pullulans KB3 was sequenced to facilitate compound isolation and identify the biosynthesis gene clusters (BGCs). This study reports the multi-omics (metabolome and genome) analysis of A. pullulans KB3, laying the foundation for discovering novel compounds of silent BGCs and identifying their biosynthesis pathway.

Dual-molecular targeting nanomedicine upregulates synergistic therapeutic efficacy in preclinical hepatoma models.

Advanced hepatocellular carcinoma (HCC) is one of the most challenging cancers because of its heterogeneous and aggressive nature, precluding the use of curative treatments. Sorafenib (SOR) is the first approved molecular targeting agent against the mitogen-activated protein kinase (MAPK) pathway for the noncurative therapy of advanced HCC; yet, any clinically meaningful benefits from the treatment remain modest, and are accompanied by significant side effects. Here, we hypothesized that using a nanomedicine platform to co-deliver SOR with another molecular targeting drug, metformin (MET), could tackle these issues. A micelle self-assembled with amphiphilic polypeptide methoxy poly(ethylene glycol)-block-poly(L-phenylalanine-co-l-glutamic acid) (mPEG-b-P(LP-co-LG)) (PM) was therefore designed for combinational delivery of two molecular targeted drugs, SOR and MET, to hepatomas. Compared with free drugs, the proposed, dual drug-loaded micelle (PM/SOR+MET) enhanced the drugs' half-life in the bloodstream and drug accumulation at the tumor site, thereby inhibiting tumor growth effectively in the preclinical subcutaneous, orthotopic and patient-derived xenograft hepatoma models without causing significant systemic and organ toxicity. Collectively, these findings demonstrate an effective dual-targeting nanomedicine strategy for treating advanced HCC, which may have a translational potential for cancer therapeutics. STATEMENT OF SIGNIFICANCE: Treatment of advanced hepatocellular carcinoma (HCC) remains a formidable challenge due to its aggressive nature and the limitations inherent to current therapies. Despite advancements in molecular targeted therapies, such as Sorafenib (SOR), their modest clinical benefits coupled with significant adverse effects underscore the urgent need for more efficacious and less toxic treatment modalities. Our research presents a new nanomedicine platform that synergistically combines SOR with metformin within a specialized diblock polypeptide micelle, aiming to enhance therapeutic efficacy while reducing systemic toxicity. This innovative approach not only exhibits marked antitumor efficacy across multiple HCC models but also significantly reduces the toxicity associated with current treatments. Our dual-molecular targeting approach unveils a promising nanomedicine strategy for the molecular treatment of advanced HCC, potentially offering more effective and safer treatment alternatives with significant translational potential.

Graphdiyne-Induced CoN/CoS2 Heterojunction: Boosting Efficiency for Bifunctional Oxygen Electrochemistry in Zinc-Air Batteries.

The performance of zinc-air battery is constrained by the sluggish rate of oxygen electrode reaction, particularly under high current discharge conditions where the kinetic process of the oxygen reduction reaction (ORR) decelerates significantly. To address this challenge, we present a novel phase transition strategy that facilitates the creation of a heteroatom-doped heterointerface (CoN/CoS2). The meticulously engineered CoN/CoS2/NC electrocatalyst displays a superior ORR half-wave potential of 0.87 V and an OER overpotential of 320 mV at 10 mA cm-2. Experimental and computational analysis confirm that the CoN/CoS2 heterostructure optimizes local charge distribution, accelerates electron transfer, and tunes active sites for enhanced catalysis. Notably, this heterojunction improves stability by resisting corrosion and degradation under harsh alkaline conditions, thus demonstrating superior performance and longevity in a custom-made liquid zinc-air battery. This research provides valuable practical and theoretical foundations for designing efficient heterointerfaces in electrocatalysis applications.

Circadian time-dependent effects of experimental colitis on theophylline disposition and toxicity.

BACKGROUND AND PURPOSE: Drug disposition undergoes significant alteration in patients with inflammatory bowel disease (IBD), yet circadian time-dependency of these changes remains largely unexplored. In this study, we aimed to determine the temporal effects of experimental colitis on drug disposition and toxicity. EXPERIMENTAL APPROACH: RNA-sequencing was used to screen genes relevant to colitis induced by dextran sodium sulfate in mice. Liver microsomes and pharmacokinetic analysis were used to analyze the activity of key enzymes. Dual luciferase assays and chromatin immunoprecipitation (ChIP) were employed to elucidate regulatory mechanisms. KEY RESULTS: RNA sequencing analysis revealed that colitis markedly influenced expression of cytochrome P450 (CYP) enzymes. Specifically, a substantial down-regulation of CYP1A2 and CYP2E1 was observed in livers of mice with colitis at Zeitgeber Time 8 (ZT8), with no significant changes detected at ZT20. At ZT8, the altered expression corresponded to diminished metabolism and enhanced incidence of hepato-cardiac toxicity of theophylline, a substrate specifically metabolized by these enzymes. A combination of assays, integrating liver-specific Bmal1 knockout and targeted activation of BMAL1 showed that dysregulation in CYP1A2 and CYP2E1 during colitis was attributable to perturbed BMAL1 functionality. Luciferase reporter and ChIP assays collectively substantiated the role of BMAL1 in regulating Cyp1a2 and Cyp2e1 transcription through its binding affinity to E-box-like sites. CONCLUSION AND IMPLICATION: Our findings establish a strong link between colitis and chronopharmacology, shedding light on how IBD affects drug disposition and toxicity over time. This research provides a theoretical foundation for optimizing drug dosage in patients with IBD.

A Novel Multi-modal Population-graph based Framework for Patients of Esophageal Squamous Cell Cancer Prognostic Risk Prediction.

Prognostic risk prediction is pivotal for clinicians to appraise the patient's esophageal squamous cell cancer (ESCC) progression status precisely and tailor individualized therapy treatment plans. Currently, CT-based multi-modal prognostic risk prediction methods have gradually attracted the attention of researchers for their universality, which is also able to be applied in scenarios of preoperative prognostic risk assessment in the early stages of cancer. However, much of the current work focuses only on CT images of the primary tumor, ignoring the important role that CT images of lymph nodes play in prognostic risk prediction. Additionally, it is important to consider and explore the inter-patient feature similarity in prognosis when developing models. To solve these problems, we proposed a novel multi-modal population-graph based framework leveraging CT images including primary tumor and lymph nodes combined with clinical, hematology, and radiomics data for ESCC prognostic risk prediction. A patient population graph was constructed to excavate the homogeneity and heterogeneity of inter-patient feature embedding. Moreover, a novel node-level multi-task joint loss was proposed for graph model optimization through a supervised-based task and an unsupervised-based task. Sufficient experimental results show that our model achieved state-of-the-art performance compared with other baseline models as well as the gold standard on discriminative ability, risk stratification, and clinical utility. The core code is available at https://github.com/wuchengyu123/MPGSurv.

Cell Membrane Hybrid Lipid Nanovesicles Enhance Innate Immunity for Synergistic Immunotherapy by Promoting Immunogenic Cell Death and cGAS Activation.

Immunotherapy shows great therapeutic potential for long-term protection against tumor relapse and metastasis. Innate immune sensors, such as cyclic GMP-AMP synthase (cGAS) and stimulator of interferon genes (STING), dissolve DNA and induce type I interferon. Through activation of the cGAS/STING pathway, chemotherapy drugs and reversine (REV) may provide synergetic anti-tumor effects. Here, we prepared drug-loaded cell membrane hybrid lipid nanovesicles (LEVs) (designated LEV@DOX@REV) by fusion of cell membranes, phospholipids, doxorubicin (DOX), and REV, to realize accurate delivery to tumors and chemo-immunotherapy. The cell membranes of LEVs confer "homing" abilities. DOX can induce immunogenic cell death as a result of its specific immunomodulatory effects, which promotes the maturation of immune cells and improves the microenvironment of the immune system. REV is proven to efficiently activate cGAS/STING signaling, thereby enhancing the immune system. The antitumor efficacy of LEV@DOX@REV was evaluated in a 4T1 subcutaneous tumor xenograft model, a distant metastatic tumor model, and a liver metastatic tumor model. LEV@DOX@REV facilitated the infiltration of cytotoxic T lymphocytes within tumors, increased the secretion of proinflammatory cytokines, and modified the tumor microenvironment. In conclusion, LEV@DOX@REV displayed favorable antitumor effects and extended the survival of tumor-bearing mice. We therefore successfully developed nanoparticles capable of enhancing immune activation that have potential therapeutic applications for cancer immunotherapy.

Experimental demonstration of wavefront reconstruction and correction techniques for variable targets based on distorted grating and deep learning.

This research presents a practical approach for wavefront reconstruction and correction adaptable to variable targets, with the aim of constructing a high-precision, general extended target adaptive optical system. Firstly, we delve into the detailed design of a crucial component, the distorted grating, simplifying the optical system implementation while circumventing potential issues in traditional phase difference-based collection methods. Subsequently, normalized fine features (NFFs) and structure focus features (SFFs) which both are independent of the imaging target but corresponded precisely to the wavefront aberration are proposed. The two features provide a more accurate and robust characterization of the wavefront aberrations. Then, a Noise-to-Denoised Generative Adversarial Network (N2D-GAN) is employed for denoising real images. And a lightweight network, Attention Mechanism-based Efficient Network (AM-EffNet), is applied to achieve efficient and high-precision mapping between features and wavefronts. A prototype of object-independent adaptive optics system is demonstrated by experimental setup, and the effectiveness of this method in wavefront reconstruction for different imaging targets has been verified. This research holds significant relevance for engineering applications of adaptive optics, providing robust support for addressing challenges within practical systems.

Ultra-sensitive measurement of small optical rotation angles using quantum entanglement based on a quasi-Wollaston prism beam splitter.

The measurement of optical rotation is fundamental to optical atomic magnetometry. Ultra-high sensitivity has been achieved by employing a quasi-Wollaston prism as the beam splitter within a quantum entanglement state, complemented by synchronous detection. Initially, we designed a quasi-Wollaston prism and intentionally rotated the crystal axis of the exit prism element by a specific bias angle. A linearly polarized light beam, incident upon this prism, is divided into three beams, with the intensity of each beam correlated through quantum entanglement. Subsequently, we formulated the equations for optical rotation angles by synchronously detecting the intensities of these beams, distinguishing between differential and reference signals. Theoretical analysis indicates that the measurement uncertainty for optical rotation angles, when using quantum entanglement, exceeds the conventional photon shot noise limit. Moreover, we have experimentally validated the effectiveness of our method. In DC mode, the experimental results reveal that the measurement uncertainty for optical rotation angles is 4.7 × 10-9 rad, implying a sensitivity of 4.7 × 10-10 rad/Hz1/2 for each 0.01 s measurement duration. In light intensity modulation mode, the uncertainty is 48.9 × 10-9 rad, indicating a sensitivity of 4.89 × 10-9 rad/Hz1/2 per 0.01 s measurement duration. This study presents a novel approach for measuring small optical rotation angles with unprecedentedly low uncertainty and high sensitivity, potentially playing a pivotal role in advancing all-optical atomic magnetometers and magneto-optical effect research.

Multi-line-of-sight Hartmann-Shack wavefront sensing based on image segmentation and K-means sorting.

Multi-line-of-sight wavefront sensing, crucial for next-generation astronomy and laser applications, often increases system complexity by adding sensors. This research introduces, to the best of our knowledge, a novel method for multi-line-of-sight Hartmann-Shack wavefront sensing by using a single sensor, addressing challenges in centroid estimation and classification under atmospheric turbulence. This method contrasts with existing techniques that rely on multiple sensors, thereby reducing system complexity. Innovations include combining edge detection and peak extraction for precise centroid calculation, improved k-means clustering for robust centroid classification, and a centroid filling algorithm for subapertures with light loss. The method's effectiveness was confirmed through simulations for a five-line-of-sight system and experimental setup for two-line and three-line-of-sight systems, demonstrating its potential in real atmospheric aberration correction conditions. Experimental findings indicate that, when implemented in a closed-loop configuration, the method significantly reduces wavefront residuals from 1 λ to 0.1 λ under authentic atmospheric turbulence conditions. Correspondingly, the quality of the far-field spot is enhanced by a factor of 2 to 4. These outcomes collectively highlight the method's robust capability in enhancing optical system performance in environments characterized by genuine atmospheric turbulence.

Design, synthesis and biological evaluation of Thiazolo[3, 2-a]Pyrimidine derivatives as novel RNase H inhibitors.

Targeting Ribonuclease H (RNase H) has been considered a viable strategy for HIV therapy. In this study, a series of novel thiazolo[3, 2-a]pyrimidine derivatives were firstly designed and synthesized as potential inhibitors of HIV-1 RNase H. Among these compounds, A28 exhibited the most potent inhibition against HIV-1 RNase H with an IC50 value of 4.14 µM, which was about 5-fold increase in potency than the hit compound A1 (IC50 = 21.49 µM). To gain deeper insights into the structure-activity relationship (SAR), a CoMFA model was constructed to yield reasonable statistical results (q2 = 0.658 and R2 = 0.969). Results from magnesium ion chelation experiments and molecular docking studies revealed that these thiazolopyrimidine inhibitors may exert their inhibitory activity by binding to an allosteric site on RNase H at the interface between subunits p51 and p66. Furthermore, this analog demonstrated favorable physicochemical properties. Our findings provide valuable groundwork for further development of allosteric inhibitors targeting HIV-1 RNase H.