Predicting molecular properties based on the interpretable graph neural network with multistep focus mechanism.

Graph neural networks based on deep learning methods have been extensively applied to the molecular property prediction because of its powerful feature learning ability and good performance. However, most of them are black boxes and cannot give the reasonable explanation about the underlying prediction mechanisms, which seriously reduce people's trust on the neural network-based prediction models. Here we proposed a novel graph neural network named iteratively focused graph network (IFGN), which can gradually identify the key atoms/groups in the molecule that are closely related to the predicted properties by the multistep focus mechanism. At the same time, the combination of the multistep focus mechanism with visualization can also generate multistep interpretations, thus allowing us to gain a deep understanding of the predictive behaviors of the model. For all studied eight datasets, the IFGN model achieved good prediction performance, indicating that the proposed multistep focus mechanism also can improve the performance of the model obviously besides increasing the interpretability of built model. For researchers to use conveniently, the corresponding website (http://graphadmet.cn/works/IFGN) was also developed and can be used free of charge.

Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency.

Protein loops play a critical role in the dynamics of proteins and are essential for numerous biological functions, and various computational approaches to loop modeling have been proposed over the past decades. However, a comprehensive understanding of the strengths and weaknesses of each method is lacking. In this work, we constructed two high-quality datasets (i.e. the General dataset and the CASP dataset) and systematically evaluated the accuracy and efficiency of 13 commonly used loop modeling approaches from the perspective of loop lengths, protein classes and residue types. The results indicate that the knowledge-based method FREAD generally outperforms the other tested programs in most cases, but encountered challenges when predicting loops longer than 15 and 30 residues on the CASP and General datasets, respectively. The ab initio method Rosetta NGK demonstrated exceptional modeling accuracy for short loops with four to eight residues and achieved the highest success rate on the CASP dataset. The well-known AlphaFold2 and RoseTTAFold require more resources for better performance, but they exhibit promise for predicting loops longer than 16 and 30 residues in the CASP and General datasets. These observations can provide valuable insights for selecting suitable methods for specific loop modeling tasks and contribute to future advancements in the field.

Real-Time pH Sensor in Bacterial Microenvironments Using Liquid Crystal Core-Shell Microspheres.

It is well-known that the bacterial microenvironment imposes restrictions on the growth and behavior of bacteria. The localized monitoring of microenvironmental factors is appreciated when consulting bacterial adaptation and behavior in the presence of chemical or mechanical stimuli. Herein, we developed a novel liquid crystal (LC) biosensor in a microsphere configuration for real-time 3D monitoring of the bacteria microenvironment, which was implemented by a microfluidic chip. As a proof of concept, a LC gel (LC-Gel) microsphere biosensor was prepared and employed in the localized pH changes of bacteria by observing the configuration change of LC under polarized optical microscopy. Briefly, the microsphere biosensor was constructed in core-shell configuration, wherein the core contained LCE7 (a nematic LC) doped with 4-pentylbiphenyl-4'-carboxylic acid (PBA), and the shell encapsulated the bacteria. The protonation of carboxyl functional groups of the PBA induced a change in charge density on the surface of LCE7 and the orientation of E7 molecules, resulting in the transitions of the LC nucleus from axial to bipolar. The developed LC-Gel microspheres pH sensor exhibited its dominant performance on localized pH real-time sensing with a resolution of 0.1. An intriguing observation from the prepared pH biosensor was that the diverse bacteria impelled distinct acidifying or alkalizing effects. Overall, the facile LC-Gel microsphere biosensor not only provides a versatile tool for label-free, localized pH monitoring but also opens avenues for investigating the effects of chemical and mechanical stimuli on cellular metabolism within bacterial microenvironments.

Polystyrene-Based Matrix to Enhance the Fluorescence of Aggregation-Induced Emission Luminogen for Fluorescence-Guided Surgery.

Achieving ultrabright fluorogens is a key issue for fluorescence-guided surgery (FGS). Fluorogens with aggregation-induced emission (AIEgens) are potential agents for FGS on the benefit of the bright fluorescence in physiological conditions. Herein, the fluorescence brightness of AIEgen is further improved by preparing the nanoparticle using a polystyrene-based matrix and utilizing it for tumor FGS with a high signal-to-background ratio. After encapsulating AIEgen into polystyrene-poly (ethylene glycol) (PS-PEG), the fluorescence intensity of the prepared AIE@PS-PEG nanoparticles is multiple times that of nanoparticles in 1, 2-distearoyl-sn-glycero-3-phosphoethanolamine-poly (ethylene glycol) (DSPE-PEG), a commonly used polymer matrix for nanoparticle preparation. Molecular dynamics simulations suggest that higher free energy is required for the outer rings of AIEgen to rotate in polystyrene than in the DSPE, indicating that the benzene rings in polystyrene can restrict the intramolecular motions of AIEgen better than the alkyl chain in DSPE-PEG. Fluorescence correlation microscopy detections suggest that the triplet excited state of AIEgens is less in PS-PEG than in DSPE-PEG. The restricted intramolecular motions and suppressed triplet excited state result in ultrabright AIE@PS-PEG nanoparticles, which are more conducive to illuminating tumor tissues in the intestine for FGS. The illumination of metastatic tumors in lungs by AIE@PS-PEG nanoparticles is also tried.

Quantitative characterization of micro-scanning imaging aliasing and optical parameter optimization.

Imaging aliasing is a common problem in the imaging domain. The aliasing of micro-scanning imaging is difficult to characterize accurately, and the matching relationship between the optical system and micro-scanning sampling is unclear. In this paper, a micro-scanning aliasing analysis model is proposed based on the property of sampling squeeze, in which the transfer functions of the optical system, detector, and digital filter are coupled with the micro-scanning sampling process. First, the imaging aliasing under different micro-scanning sampling modes is evaluated based on the constraint relationship of the transfer functions for each part. The stretch factor of the transfer function under micro-scanning sampling is calculated by utilizing the amount of aliasing. Second, the micro-scanning imaging transfer function under different optical parameters is predicted by the stretch factor, and the results indicate the existence of an optimal F-number that maximizes the micro-scanning performance improvement. Furthermore, the optimal micro-scanning imaging F-numbers for different fill factors are given, and the matching relationship between optical parameters, fill factors and micro-scanning mode is analyzed. Finally, a micro-scanning imaging simulation is performed based on the actual imaging transfer and micro-scanning sampling process. The simulation experiment verifies the accuracy of the micro-scanning aliasing model and gives the consistent test results of the optimal F-number. This paper can provide theoretical support for the matching relationship among micro-scanning imaging parameters, which is of great significance for the refined optimal design of micro-scanning imaging systems.

Sub-pixel target fine spatial feature extraction method based on aperture coding and micro-scanning imaging mechanism.

The small imaging size of targets over long distances results in the loss of geometry and spatial features. Current methods are subject to sampling limitations and cannot accurately capture the spatial features of sub-pixel targets. This paper proposes a method to accurately locate and extract the fine spatial features of sub-pixel targets through aperture coding and micro-scanning imaging. First, the formation mechanism of imaging features for sub-pixel targets is analyzed. Second, the optical aperture is anisotropically coded in different directions to modulate the spreading spots of the target. The primary spreading direction and the center of the anisotropic spreading spots are extracted. The contour and the location of the target are determined from the spreading length and the intersections of the primary spreading directions. Then, the target is sampled by different detector units through various micro-scanning offsets. The pixel units containing different sub-pixel components of the target after offset are determined based on the location results. The fine spatial distribution of the sub-pixel target is reconstructed based on the intensity variations in the pixel units containing the target. Finally, the accuracy of the sub-pixel target fine spatial feature extraction method is validated. The results show a sub-pixel localization error of less than 0.02 and an effective improvement of the sub-pixel target spatial resolution. This paper provides significant potential for improving the ability to capture spatial features of targets over long distances.

Imaging performance prediction of a hypersonic target in a geosynchronous orbit based on multi-dimensional information correlation of radiation, multi-spectral, and polarization.

Hypersonic target detection based on infrared intensity characteristics is easily disturbed by sea surface and cloud flares when detected by space-based optical systems, which results in a low detection rate, high false alarm, and difficulty in stable detection. This paper explores a method to improve target detection performance based on the correlation of infrared radiation, multi-spectral and polarization. Firstly, the comprehensive factors that influence complex ambient illumination, atmospheric transmission, and clutter background on spectral-polarization characteristics of hypersonic targets are analyzed. Based on the global radiation scattering theory, the temperature distribution model of the hypersonic target is established by using FLUENT. The polarization emission and pBRDF model of the target is established, and the radiation polarization transfer model is generated. Secondly, the sea surface temperature distribution is obtained by inversion of Landsat8 remote sensing data. The radiation polarization transfer model of the sea surface is established based on the Cox-Munk model combined with pBRDF and the polarization emission model. Thirdly, the polarization scattering effect of atmospheric particles on the upward radiation of the interaction of the target with the sunlight is considered comprehensively, and the 6SV radiative transfer model is used to calculate the polarization effect of atmospheric particles on the upward radiation transmission of the target and the background. Then, combined with the point diffusion of the optical system and the photoelectric conversion of the detector, the multi-dimensional full-chain imaging prediction model of the hypersonic target-sea background-ambient atmosphere-optical system-detector is established. The imaging characteristics and detection performance of the target in different imaging dimensions are simulated and analyzed with the signal-to-clutter ratio (SCR). The research shows that in the direction of reflected sunlight from the sea surface, the sea surface glare is suppressed and the target is highlighted through a target detection method of multi-dimensional information. This method has better detection results than the infrared multi-spectral detection method.

Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein-Cyclic Peptide Complexes.

Cyclic peptides have emerged as a highly promising class of therapeutic molecules owing to their favorable pharmacokinetic properties, including stability and permeability. Currently, many clinically approved cyclic peptides are derived from natural products or their derivatives, and the development of molecular docking techniques for cyclic peptide discovery holds great promise for expanding the applications and potential of this class of molecules. Given the availability of numerous docking programs, there is a pressing need for a systematic evaluation of their performance, specifically on protein-cyclic peptide systems. In this study, we constructed an extensive benchmark data set called CPSet, consisting of 493 protein-cyclic peptide complexes. Based on this data set, we conducted a comprehensive evaluation of 10 docking programs, including Rosetta, AutoDock CrankPep, and eight protein-small molecule docking programs (i.e., AutoDock, AudoDock Vina, Glide, GOLD, LeDock, rDock, MOE, and Surflex). The evaluation encompassed the assessment of the sampling power, docking power, and scoring power of these programs. The results revealed that all of the tested protein-small molecule docking programs successfully sampled the binding conformations when using the crystal conformations as the initial structures. Among them, rDock exhibited outstanding performance, achieving a remarkable 94.3% top-100 sampling success rate. However, few programs achieved successful predictions of the binding conformations using tLEaP-generated conformations as the initial structures. Within this scheme, AutoDock CrankPep yielded the highest top-100 sampling success rate of 29.6%. Rosetta's scoring function outperformed the others in selecting optimal conformations, resulting in an impressive top-1 docking success rate of 87.6%. Nevertheless, all the tested scoring functions displayed limited performance in predicting binding affinity, with MOE@Affinity dG exhibiting the highest Pearson's correlation coefficient of 0.378. It is therefore suggested to use an appropriate combination of different docking programs for given tasks in real applications. We expect that this work will offer valuable insights into selecting the appropriate docking programs for protein-cyclic peptide complexes.

Evaluation of AlphaFold2 Structures for Hit Identification across Multiple Scenarios.

The introduction of AlphaFold2 (AF2) has sparked significant enthusiasm and generated extensive discussion within the scientific community, particularly among drug discovery researchers. Although previous studies have addressed the performance of AF2 structures in virtual screening (VS), a more comprehensive investigation is still necessary considering the paramount importance of structural accuracy in drug design. In this study, we evaluate the performance of AF2 structures in VS across three common drug discovery scenarios: targets with holo, apo, and AF2 structures; targets with only apo and AF2 structures; and targets exclusively with AF2 structures. We utilized both the traditional physics-based Glide and the deep-learning-based scoring function RTMscore to rank the compounds in the DUD-E, DEKOIS 2.0, and DECOY data sets. The results demonstrate that, overall, the performance of VS on AF2 structures is comparable to that on apo structures but notably inferior to that on holo structures across diverse scenarios. Moreover, when a target has solely AF2 structure, selecting the holo structure of the target from different subtypes within the same protein family produces comparable results with the AF2 structure for VS on the data set of the AF2 structures, and significantly better results than the AF2 structures on its own data set. This indicates that utilizing AF2 structures for docking-based VS may not yield most satisfactory outcomes, even when solely AF2 structures are available. Moreover, we rule out the possibility that the variations in VS performance between the binding pockets of AF2 and holo structures arise from the differences in their biological assembly composition.

Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes.

Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.

Trimester-specific effect of maternal co-exposure to organophosphate esters and phthalates on preschooler cognitive development: The moderating role of gestational vitamin D status.

Organophosphate esters (OPEs) and phthalate acid esters (PAEs) are prevalent endocrine-disrupting chemicals (EDCs). Humans are often exposed to OPEs and PAEs simultaneously through multiple routes. Given that fetal stage is a critical period for neurodevelopment, it is necessary to know whether gestational co-exposure to OPEs and PAEs affects fetal neurodevelopment. However, accessible epidemiological studies are limited. The present study included 2, 120 pregnant women from the Ma'anshan Birth Cohort (MABC) study. The concentrations of tris (2-chloroethyl) phosphate (TCEP), 6 OPE metabolites and 7 PAE metabolites were measured in the first, second and third trimester using ultra-performance liquid chromatography-tandem mass spectrometry (LC-MS). Cognitive development of preschooler was assessed based on the Wechsler Preschool and Primary Scale of Intelligence-Fourth Edition (WPPSI-IV) of the Chinese version. Generalized estimating equations (GEEs), restricted cubic spline (RCS) and generalized additive models (GAMs) were employed to explore the associations between individual OPE exposure and preschooler cognitive development. The quantile-based g-computation (QGC) method was used to estimate the joint effect of PAEs and OPEs exposure on cognitive development. GEEs revealed significant adverse associations between diphenyl phosphate (DPHP) (ß: -0.58, 95% CI: -1.14, -0.01), bis (2-butoxyethyl) phosphate(BBOEP) (ß: -0.44, 95% CI: -0.85, -0.02), bis(1-chloro-2-propyl) phosphate (BCIPP) (ß: -0.81, 95%CI: -1.43, -0.20) and full-scale intelligence quotient (FSIQ) in the first trimester; additionally, TCEP and bis(2-ethylhexyl) phosphate (BEHP) in the second trimester, as well as DPHP in the third trimester, were negatively associated with cognitive development. Through the QGC analyses, mixture exposure in the first trimester was negatively associated with FSIQ scores (ß: -1.70, 95% CI: -3.06, -0.34), mono-butyl phthalate (MBP), BCIPP, and DPHP might be the dominant contributors after controlling for other OPEs and PAEs congeners. Additionally, the effect of OPEs and PAEs mixture on cognitive development might be driven by vitamin D deficiency.

MSC microvesicles loaded G-quadruplex-enhanced circular single-stranded DNA-9 inhibits tumor growth by targeting MDSCs.

BACKGROUND: Myeloid-derived suppressor cells (MDSCs) promote tumor growth, metastasis, and lead to immunotherapy resistance. Studies revealed that miRNAs are also expressed in MDSCs and promote the immunosuppressive function of MDSCs. Currently, few studies have been reported on inducible cellular microvesicle delivery of nucleic acid drugs targeting miRNA in MDSCs for the treatment of malignant tumors. RESULTS AND CONCLUSION: In this study, we designed an artificial DNA named G-quadruplex-enhanced circular single-stranded DNA-9 (G4-CSSD9), that specifically adsorbs the miR-9 sequence. Its advanced DNA folding structure, rich in tandem repeat guanine (G-quadruplex), also provides good stability. Mesenchymal stem cells (MSCs) were prepared into nanostructured vesicles by membrane extrusion. The MSC microvesicles-encapsulated G4-CSSD9 (MVs@G4-CSSD9) was delivered into MDSCs, which affected the downstream transcription and translation process, and reduced the immunosuppressive function of MDSCs, so as to achieve the purpose of treating melanoma. In particular, it provides an idea for the malignant tumor treatment.

Optimization of the Extraction Process and Biological Activities of Triterpenoids of Schisandra sphenanthera from Different Medicinal Parts and Growth Stages.

Schisandra sphenanthera Rehd. et Wils., as a traditional Chinese medicine, has important medicinal value. In the market, the availability of the fruit of S. sphenanthera mainly relies on wild picking, but many canes and leaves are discarded during wild collection, resulting in a waste of resources. The canes and leaves of S. sphenanthera contain various bioactive ingredients and can be used as spice, tea, and medicine and so present great utilization opportunities. Therefore, it is helpful to explore the effective components and biological activities of the canes and leaves to utilize S. sphenanthera fully. In this study, the response surface method with ultrasound was used to extract the total triterpenoids from the canes and leaves of S. sphenanthera at different stages. The content of total triterpenoids in the leaves at different stages was higher than that in the canes. The total triterpenoids in the canes and leaves had strong antioxidant and antibacterial abilities. At the same time, the antibacterial activity of the total triterpenoids against Bacillus subtilis and Pseudomonas aeruginosa was stronger than that against Staphylococcus aureus and Escherichia coli. This study provides the foundation for the development and utilization of the canes and leaves that would relieve the shortage of fruit resources of S. sphenanthera.

Functional Liquid Crystal Core/Hydrogel Shell Microcapsules for Monitoring Live Cells in a 3D Microenvironment.

Three-dimensional (3D) cell culture, even as a simple microspheroid model, can be used to recapitulate the native biological microenvironment of cells. Examining the biochemical characteristics of cells in multicellular hydrogel microspheroids using microsensors is usually limited to monitoring the medium around the microspheroids. Here, functional liquid crystal (LC) core/hydrogel shell microcapsules loaded with cells were prepared using droplet microfluidic technology for monitoring live cells in a 3D microenvironment. These microcapsules have a distinctive core/shell structure; cells can be cultured in the hydrogel shell of this 3D model. The functional LC core responds to the acidic microenvironment of cells, showing an axial-to-bipolar transfiguration. 3D cell culture and visual monitoring of the cell microenvironment can be simultaneously achieved in a single microcapsule. Therefore, this novel method may enable a standard approach for monitoring multiple ions or molecules in a 3D model of the cell microenvironment.

Microfluidic Sampling of Undissolved Components from Subcellular Regions of Living Single Cells for Mass Spectrometry Analysis.

Precise sampling of undissolved chemical components from subcellular regions of living single cells is a prerequisite for their in-depth analysis, which could promote understanding of subtle early stage physiological or pathological processes. Here we report a microfluidic method to extract undissolved components from subcellular regions for MS analysis. The target single cell was isolated by the microchamber beneath the microfluidic probe and washed by the injected biocompatible isotonic glucose aqueous solution (IGAS). Then, the sampling solvent was injected to extract undissolved components from the expected subcellular region of the living single cell, where the position and size of the sampling region could be controlled. The components immobilized by undissolved cellular structures were proven to be successfully extracted. Since unextracted subcellular regions were protected by IGAS, the single cell could survive after a tiny part was extracted, providing the possibility of repetitive sampling of the same living cell. Phospholipids extracted from the subcellular regions were successfully identified. The results demonstrated the feasibility of our method for subcellular sampling and analysis.

Overcoming the Dilemma of Permeability and Stability of Polymersomes through Traceless Cross-Linking.

In nature, cells are highly compartmentalized into many organelles that are well separated from the rest of the cellular space by unique membrane structures, which are of crucial importance to allow cells to perform various physiological functions in such a small and crowded space. Learning from the ubiquitous membrane structures of cells and organelles has continuously inspired the development of artificial self-assembled nanostructures, with lipid vesicles (liposomes) and polymer vesicles (polymersomes) being the most representative examples. Similar to the membrane-bound structures of cells and organelles, both liposomes and polymersomes contain an aqueous interior enclosed by a bilayer membrane. Therefore, liposomes and polymersomes have been extensively investigated to mimic the fundamental structures and functions of living cells. For example, liposomes and polymersomes have been successfully engineered as nanocarriers, smart nanoreactors, artificial organelles, and so on. Notably, living cells can exchange both energy and materials with surrounding environments, benefiting from the selective permeability of lipid membranes. The permselectivity of cell membranes is thus an essential attribute of living organisms. Compared to liposomes, polymersomes have increased structural stability but low membrane permeability. Indeed, polymersomes are almost impermeable to small molecules, ions, and even water molecules. To improve the permeability of polymersomes, much effort has been devoted to the incorporation of channel proteins, the coassembly of oppositely charged block copolymers (BCPs), the development of stimuli-responsive BCPs, and so on. Despite great achievements, these approaches generally lead to decreased stability of polymersomes and, sometimes, polymersome disintegration. In this Account, we discuss our recent efforts to reconcile the stability and permeability of polymersomes via a traceless cross-linking approach. Although cross-linking reactions within bilayer membranes generally lead to decreased permeability, the traceless cross-linking approach can concurrently improve the stability and permeability of polymersomes. Specifically, stimuli-responsive polymersomes undergo either covalent cross-linking or noncovalent cross-linking reactions under specific stimuli to increase bilayer stability, while the cross-linking processes can concurrently permeabilize polymersome bilayers through cross-linking-driven hydrophobic-to-hydrophilic transitions. Notably, unlike conventional cross-linking processes requiring additional cross-linkers, the traceless cross-linking process does not involve extra cross-linking agents but takes full advantage of the in situ generated active moieties. By taking advantage of the simultaneous modulation of the stability and permeability of polymersomes via traceless cross-linking, these polymersomes can be further engineered as smart nanocarriers and nanoreactors. The robustness and generality of this approach have been validated by both extracellular and intracellular stimuli such as light irradiation, glutathione, and hydrogen peroxide. Moreover, many functional groups such as fluorescent dyes and contrast agents can be integrated into this versatile platform as well, enabling the construction of theranostic nanovectors capable of responding to pathological microenvironments. This Account provides a new approach to regulating the permeability of polymersomes while maintaining their structural stability.

Imaging quality characterization and optimization of segmented planar imaging system based on honeycomb dense azimuth sampling lens array.

Segmented planar photoelectric imaging is an advanced computational imaging technology that utilizes photonic integrated circuits (PICs) to achieve the miniaturization of imaging systems. The original radial-spoke lens array has dense radial sampling and coarse azimuthal sampling. The sparsity and inhomogeneity of spatial frequency sampling lead to the loss of spatial frequency information and blurred reconstructed images. In this paper, a honeycomb dense azimuth sampling lens array is proposed, and three baseline pairing methods are designed, which can realize dense azimuth sampling, effectively increase spatial frequency sampling and improve the imaging quality. The signal transmission model of the segmented planar imaging system is established and the imaging process is simulated and analyzed. The simulation results show that the honeycomb lens array improves the azimuth sampling density and spatial frequency coverage, and its imaging quality is significantly improved compared with the hexagonal lens array and the radial-spoke lens array. Furthermore, the optimal choice of the baseline pairing method and the error range of the fill factor and are also given in this paper. The results indicate that the mixed pairing method first ensures low and medium-frequency dense sampling, and then increases high-frequency sampling, which makes the imaging results better than those of the other two baseline pairing methods in terms of image contour, contrast and image detail information. The sampling density of the spatial frequency and the imaging quality can be improved by increasing the fill factor. In the actual manufacturing process, the allowable error range of the fill factor of the lens array is within 5%. The research results will provide theoretical support for the design and development of segmented planar imaging system.

Accurate characterization of full-chain infrared multispectral imaging features under an aerodynamic thermal environment.

The detection performance of infrared imaging systems during high-speed flight is significantly impacted by aero-optical and aero-thermal radiation effects. However, traditional numerical calculations struggle to balance accuracy and efficiency, and there is a lack of a comprehensive model for infrared imaging in an aerodynamic thermal environment. In this study, we propose a calculation method based on Cellular Automata (CA) ray tracing, which allows for parallel calculation of aero-optical and aero-thermal radiation effects by combining optical field transport rules with the cellular space obtained by interpolation under fluid-solid boundary constraints. Using this method, we extend the traditional imaging feature prediction model of the infrared imaging system to obtain an accurate characterization model of the full-chain imaging features adapted to the aerodynamic thermal environment. Finally, we investigate the characteristics of infrared multispectral imaging system in various spectral bands under the influence of aero-optical and aero-thermal radiation effects. With this full-chain imaging model, the key elements of the imaging system under aerodynamic thermal environment can be globally optimized.

Pathological oligodendrocyte precursor cells revealed in human schizophrenic brains and trigger schizophrenia-like behaviors and synaptic defects in genetic animal model.

Although the link of white matter to pathophysiology of schizophrenia is documented, loss of myelin is not detected in patients at the early stages of the disease, suggesting that pathological evolution of schizophrenia may occur before significant myelin loss. Disrupted-in-schizophrenia-1 (DISC1) protein is highly expressed in oligodendrocyte precursor cells (OPCs) and regulates their maturation. Recently, DISC1-Δ3, a major DISC1 variant that lacks exon 3, has been identified in schizophrenia patients, although its pathological significance remains unknown. In this study, we detected in schizophrenia patients a previously unidentified pathological phenotype of OPCs exhibiting excessive branching. We replicated this phenotype by generating a mouse strain expressing DISC1-Δ3 gene in OPCs. We further demonstrated that pathological OPCs, rather than myelin defects, drive the onset of schizophrenic phenotype by hyperactivating OPCs' Wnt/ß-catenin pathway, which consequently upregulates Wnt Inhibitory Factor 1 (Wif1), leading to the aberrant synaptic formation and neuronal activity. Suppressing Wif1 in OPCs rescues synaptic loss and behavioral disorders in DISC1-Δ3 mice. Our findings reveal the pathogenetic role of OPC-specific DISC1-Δ3 variant in the onset of schizophrenia and highlight the therapeutic potential of Wif1 as an alternative target for the treatment of this disease.

Production and Reconfiguration of Double Emulsions by Temperature Control.

Double emulsions are of great importance for both science and engineering. However, the production of multicore double-emulsion droplets is challenging and normally requires sophisticated microfluidic devices, which limits their availability to broader communities. Here, we propose a simple, precise, and scalable batch method for producing double emulsions with monodispersed multicores at milliliter per minute rates, using the most common means in laboratory, temperature. By rapidly cooling liquid crystal emulsions, the introduced temperature gradient around the emulsion droplets leads to the injection of monodispersed guest droplets to form double-emulsion droplets. The number of injected water droplets can be precisely controlled by adjusting the thermally induced mechanical force through the temperature difference and the cooling rate. In contrast to conventional microfluidic fabrication, this method processes all emulsion droplets simultaneously in a noncontact and in situ manner. Therefore, it has great flexibility, allows multiple processing of double emulsions of arbitrary shape, has good capacity for mass production, and offers excellent compatibility with technologies such as microfluidics. Finally, we demonstrate that temperature changes can also be used to release the inner droplets from the double emulsion. The proposed method offers a reversible tool for processing double emulsions with minimal cost and expertise and is applicable to droplet-based microsystems in materials science, photonics, sensors, pharmaceuticals, and biotechnology.