Age-related alterations in myelin are a prominent feature of brain aging, yet how myelin-associated markers and oligodendrocyte lineage cell populations change across the primate lifespan remains incompletely characterized. Here, we provide a multimodal, cross-sectional analysis of myelin-related imaging and cellular markers in the prefrontal cortex (PFC) of age-matched both male and female rhesus macaques across postnatal development and aging using a multimodal approach combining magnetic resonance imaging (MRI), histological analysis, immunohistochemistry, and RNAscope in situ hybridization. We quantified regional gray and white matter volumes and myelin water fraction measures in prefrontal cortex (PFC) subregions BA9 and BA46 across four age groups: 5, 10, 15, and 30 years. Myelin water fraction and regional brain volumes exhibited age-dependent increases from childhood through adolescence, peaking at 15 years, followed by a decline in aged animals. Histological analyses revealed age-associated changes in myelin organization and the presence of myelin fragments within Iba1-positive microglia, along with dynamic alterations in the density of cells expressing oligodendrocyte lineage-associated markers, including Olig2 and oligodendrocyte precursor cell (OPC)-associated markers, in BA9 and BA46. OPC density displayed a nonlinear, ageassociated pattern across developmental and aging stages, coinciding temporally with changes in myelin-associated imaging measures. Our findings define an age-related framework of myelin alterations and oligodendrocyte lineage markers in the primate prefrontal cortex. This work establishes a reference dataset for oligodendrocyte lineage dynamics across the lifespan in a translationally relevant primate model, providing a foundation for future mechanistic and interventional studies of myelin maintenance during brain aging.

Understanding the dynamic evolution of protein structures is crucial for uncovering their biological functions. Yet, real-time prediction of these dynamic structures remains a significant challenge. Two-dimensional infrared (2DIR) spectroscopy is a powerful tool for analyzing protein dynamics. However, translating its complex, low-dimensional signals into detailed three-dimensional structures is a daunting task. In this study, we introduce a machine learning-based approach that accurately predicts dynamic three-dimensional protein structures from 2DIR descriptors. Our method establishes a robust "spectrum-structure" relationship, enabling the recovery of three-dimensional structures across a wide variety of proteins. It demonstrates broad applicability in predicting dynamic structures along different protein folding trajectories, spanning timescales from microseconds to milliseconds. This approach also shows promise in identifying the structures of previously uncharacterized proteins based solely on their spectral descriptors. The integration of AI with 2DIR spectroscopy offers insights and represents a significant advancement in the real-time analysis of dynamic protein structures.

The structurally sensitive amide II infrared (IR) bands of proteins provide valuable information about the hydrogen bonding of protein secondary structures, which is crucial for understanding protein dynamics and associated functions. However, deciphering protein structures from experimental amide II spectra relies on time-consuming quantum chemical calculations on tens of thousands of representative configurations in solvent water. Currently, the accurate simulation of amide II spectra for whole proteins remains a challenge. Here, we present a machine learning (ML)-based protocol designed to efficiently simulate the amide II IR spectra of various proteins with an accuracy comparable to experimental results. This protocol stands out as a cost-effective and efficient alternative for studying protein dynamics, including the identification of secondary structures and monitoring the dynamics of protein hydrogen bonding under different pH conditions and during protein folding process. Our method provides a valuable tool in the field of protein research, focusing on the study of dynamic properties of proteins, especially those related to hydrogen bonding, using amide II IR spectroscopy.

ABSTRACTBackgroundThe mechanism driving multiple pathophysiological alterations in Alzheimer’s disease (AD) remains unclear. Thiamine deficiency, a well-known feature of AD, may contribute to these alterations.MethodsThe expressions of four known genes associated with thiamine metabolism were studied in brain samples from patients with AD and other neurodegenerative disorders. The results were further demonstrated in AD and diabetic mouse and cellular models. The phenotypes of mice with conditionalThiamine pyrophosphokinase-1(Tpk) knockout in brain excitatory neurons were investigated. The therapeutic effects of thiamine diphosphate supplement andTpkdelivery on cellular and mouse models were explored. Phase 2 clinical trial of benfotiamine, a thiamine derivative, plus donepezil was performed.ResultsOnly TPK expression was inhibited in brain samples of AD patients, while none of thiamine-associated genes were significantly changed in other neurodegenerative disorders. TPK inhibition in the brains and neurons was verified in AD and diabetic mouse and cellular models. Mice withTpkdeletion in neurons exhibited all major pathophysiological alterations of AD, including amyloid deposition, Tau hyperphosphorylation, and brain atrophy. TPK expression restoration and thiamine diphosphate supplement ameliorated the pathophysiological and behavioral phenotypes in mouse and cell models withTpkinsufficiency. Benfotiamine delayed cognitive decline in mild-to-moderate AD patients with a dose-effect relationship, particularly with a significant attenuation of the deterioration in moderate AD patients by post hoc analysis.ConclusionsTPK deficiency and hence thiamine diphosphate reduction in neurons are a decisive factor driving multiple pathophysiologic alterations of AD, unveiling a new direction for the disease mechanism and treatment.

Two-dimensional electronic spectroscopy (2DES) has proven to be a highly effective technique in studying the properties of excited states and the process of excitation energy transfer in complex molecular assemblies, particularly in biological light-harvesting systems. However, the accurate simulation of 2DES for large systems still poses a challenge because of the heavy computational demands it entails. In an effort to overcome this limitation, we devised a coarse-grained 2DES method. This method encompasses the treatment of the entire system by dividing it into distinct weakly coupled segments, which are assumed to communicate predominantly through incoherent exciton transfer. We first demonstrate the efficiency of this method through simulation on a model dimer system, which demonstrates a marked improvement in calculation efficiency, with results that exhibit good concordance with reference spectra calculated with less approximate methods. Additionally, the application of this method to the light-harvesting antenna 2 (LH2) complex of purple bacteria showcases its advantages, accuracy, and limitations. Furthermore, simulating the anisotropy decay in LH2 induced by energy transfer and its comparison with experiments confirm that the method is capable of accurately describing dynamical processes in a biologically relevant system. This method presented lends itself to an extension that accounts for the effect of intrasegment relaxation processes on the 2DES spectra, which for computational efficiency are ignored in the implementation reported here. It is envisioned that the method will be employed in the future to accurately and efficiently calculate 2D spectra of more extensive systems, such as photosynthetic supercomplexes.

We present the implementation of the time-domain multichromophoric fluorescence resonant energy transfer (TC-MCFRET) approach in the numerical integration of the Schrodinger equation (NISE) program. This method enables the efficient simulation of incoherent energy transfer between distinct segments within large and complex molecular systems, such as photosynthetic complexes. Our approach incorporates a segmentation protocol to divide these systems into manageable components and a modified thermal correction to ensure detailed balance. The implementation allows us to calculate the energy transfer rate in the NISE program systematically and easily. To validate our method, we applied it to a range of test cases, including parallel linear aggregates and biologically relevant systems like the B850 rings from LH2 and the Fenna-Matthews-Olson complex. Our results show excellent agreement with previous studies, demonstrating the accuracy and efficiency of our TD-MCFRET method. We anticipate that this approach will be widely applicable to the calculation of energy transfer rates in other large molecular systems and will pave the way for future simulations of multidimensional electronic spectra.

Viewpoint planning is crucial in industrial 3D measurement, significantly affecting both efficiency and stability. However, traditional visibility-based methods for assessing measurable areas encounter substantial challenges when dealing with highly reflective parts. These challenges include: 1) An inadequate analysis of the imaging mechanisms for structured light and highly reflective parts, resulting in inaccurate calculations of measurable areas; 2) A reliance on fixed measurement parameters, which substantially diminishes the actual measurable area relative to the theoretical maximum, thereby necessitating an increased number of viewpoints. To overcome these challenges, we propose an optimization strategy for measurability assessment in the viewpoint planning of highly reflective parts. We first involve study the interaction between structured light and highly reflective surfaces, leading to the development of a measurability assessment model that integrates visibility, measurable angles, and well-exposedness to ensure precise calculation of measurable areas. Furthermore, we develop a binocular optimal exposure selection network (OESN) that considers the binocular camera's reflection coefficient to select the ideal exposure time, thereby enlarging the measurable area of a single viewpoint to optimize overall measurement efficiency. Experimental results confirm that our approach closely matches the predefined constraint (95.74% versus 95%), while reducing the number of required viewpoints to 2/3 compared to the fixed exposure method.

Limited by the imaging dynamic range of the camera, the phenomenon of over-exposure and over-dark often occurs in the 3D measurement of strong reflective sheet metal parts, resulting in incomplete measurement result. One of existing methods such as multiple exposure can measure most of the visible area under a single viewpoint, but the visible area with too small or too large incidence angle still cannot be measured. To solve this problem, in this paper, a method of viewpoint planning for sheet metal parts with strong reflection is proposed. The method introduces the surface reflection model of reflective sheet metal parts into viewpoint planning to achieve the synchronous optimum of measurement efficiency and data integrity. Firstly, according to the measurable region of the surface structured light 3D measurement system and CAD model, the candidate viewpoint set is randomly generated in the sampling space, and the visibility matrix is constructed by analyzing whether each candidate viewpoint is visible to each patch of the model. Then, the surface reflection model of sheet metal parts with strong reflection is constructed, and the reflection coefficient of the visible patches under each viewpoint is calculated according to the reflection model. Based on this, the measurability of the visible patches of the viewpoint under multiple exposures is calculated, and the visibility matrix is updated. Lastly, through the viewpoint quality evaluation function constructed based on the data coverage increment and multiple-exposure time, the viewpoint with the highest quality is selected heuristically until the coverage requirement is met. Experiments show that the algorithm can improve the measurement efficiency and ensure the integrity of the measurement data.

The automatic 3D measurement technology based on surface structured light has been widely used in the production of metal sheet parts. Before the measurement, it is necessary to manually plan the robot's measurement viewpoint and path, and each viewpoint needs to take into account the measurement quality of the shape surface and holes at the same time. The planning is very difficult, and manual planning results in long offline debugging time and low measurement efficiency. To address this problem, this paper proposes a view planning method that focuses on holes of metal sheet part. Firstly, the relationship between view angle and hole reconstruction quality is constructed to analyze the influence of view angle on hole measurement. Then, candidate viewpoints, which meet the visibility and hole reconstruction quality requirement, are generated. Finally, the Markov decision process is used to model the coverage planning problem, and the Monte Carlo tree search method is used to generate the shortest viewpoint sequence which meets the coverage ratio requirements. The experimental results show that the method proposed in this paper can realize the complete measurement of complex surface features such as holes, and plan an efficient robotic measurement path.

Aiming at the assembly of workpieces with multiple plane structures, this paper proposes a 3D visual positioning and robot assembly method. First, several planes are extracted by plane detection and outlier removal algorithms; then the poses of the plane point clouds are calculated by the oriented bounding box algorithm; finally, the target plane is found through the length and width constraints. Based on proper hand-eye calibration and motion planning, the method can achieve the workpieces’ recognizing, positioning and assembly accurately, meanwhile has high computational efficiency and strong robustness. As shown in the experiment results, for the recognition and positioning accuracy, the translation error of the proposed method is less than 0.2mm, and the rotation error is less than 0.2°; for the robot assembly accuracy, the translation error of the proposed method is less than 0.8mm and the rotation error is less than 0.4°. Therefore, the proposed method can successfully satisfy the requirement of robot assembly tasks.

The geometric dimensions and tolerances of blades, which are critical parts of turbomachinery with complex features, must be strictly controlled to ensure the efficiency and safety of the engine. Optical-based inspection systems for blades are increasingly receiving attention because of their high efficiency and flexibility. However, as a key issue in blade inspection, the matching of the part coordinate system and machine coordinate system directly determines the measurement accuracy and automation. The blade surface is complex and has no obvious features, and accurate and rapid matching thus remains a challenging problem to solve. To overcome these problems and realize the accurate inspection of blade profiles, an automatic and high-accuracy matching method for a blade measurement system integrating fringe projection profilometry (FPP) and conoscopic holography (CH) is proposed in this paper. First, automatic rough matching is realized making use of the ability of FPP to quickly obtain high-resolution cloud of points and improving the four-point congruent sets algorithm. The path of the CH measurement based on the calibration and rough matching result is then planned, to sample high-precision and uniform cloud-of-points data on the blade surface. Finally, a fine matching optimization algorithm is implemented with the signal-to-noise ratio as the weight. The results of simulation experiments and inspection case studies demonstrate that the proposed matching method is efficient and accurate.

PURPOSE Preclinical MR fingerprinting (MRF) suffers from long acquisition time for organ-level coverage due to demanding image resolution and limited undersampling capacity. This study aims to develop a deep learning-assisted fast MRF framework for sub-millimeter T1 and T2 mapping of entire macaque brain on a preclinical 9.4 T MR system. METHODS Three dimensional MRF images were reconstructed by singular value decomposition (SVD) compressed reconstruction. T1 and T2 mapping for each axial slice exploited a self-attention assisted residual U-Net to suppress aliasing-induced quantification errors, and the transmit-field (B1 + ) measurements for robustness against B1 + inhomogeneity. Supervised network training used MRF images simulated via virtual parametric maps and a desired undersampling scheme. This strategy bypassed the difficulties of acquiring fully sampled preclinical MRF data to guide network training. The proposed fast MRF framework was tested on experimental data acquired from ex vivo and in vivo macaque brains. RESULTS The trained network showed reasonable adaptability to experimental MRF images, enabling robust delineation of various T1 and T2 distributions in the brain tissues. Further, the proposed MRF framework outperformed several existing fast MRF methods in handling the aliasing artifacts and capturing detailed cerebral structures in the mapping results. Parametric mapping of entire macaque brain at nominal resolution of 0.35 × $$ \times $$ 0.35 × $$ \times $$ 1 mm3 can be realized via a 20-min 3D MRF scan, which was sixfold faster than the baseline protocol. CONCLUSION Introducing deep learning to MRF framework paves the way for efficient organ-level high-resolution quantitative MRI in preclinical applications.

Holes are common and important feature information in sheet metal parts and play a decisive role in product production and assembly. The 3D contour reconstruction method based on passive binocular vision has been widely used in hole measurement due to its high accuracy and robustness. It can obtain dimensional data such as diameter and center of round holes, but there are still problems in measurement accuracy and integrity, and it is difficult to accurately measure special-shaped holes. Aiming at these issues, a fringe projection-based 3D contour measurement method for holes on sheet metal parts is proposed. In this method, two constant movements are performed on the extracted contour curve to the surrounding area, and the 3D contour points can be reconstructed according to the phase matching. Therefore, the real 3D contour is reconstructed inversely according to the corresponding points of the previous two reconstructed contours. With this method, contours only need to be extracted on one image, so it can reduce the effect of inconsistency in contour extraction. In addition, the accuracy and integrity of the 3D contour can be guaranteed by using high-quality phase matching of the sheet metal area. The experimental results show that this method can achieve accurate and complete 3D contour reconstruction for sheet metal holes of various shapes, and the accuracy can reach 0.08mm.

The excitation energy transfer (EET) process for photosynthetic antenna complexes consisting of subunits, each comprised of multiple chromophores, remains challenging to describe. The multichromophoric Förster resonance energy transfer theory is a popular method to describe the EET process in such systems. This paper presents a new time-domain method for calculating energy transfer based on the combination of multichromophoric Förster resonance energy transfer theory and the Numerical Integration of the Schrödinger Equation method. After validating the method on simple model systems, we apply it to the Light-Harvesting antenna 2 (LH2) complex, a light harvesting antenna found in purple bacteria. We use a simple model combining the overdamped Brownian oscillators to describe the dynamic disorder originating from the environmental fluctuations and the transition charge from the electrostatic potential coupling model to determine the interactions between chromophores. We demonstrate that with this model, both the calculated spectra and the EET rates between the two rings within the LH2 complex agree well with experimental results. We further find that the transfer between the strongly coupled rings of neighboring LH2 complexes can also be well described with our method. We conclude that our new method accurately describes the EET rate for biologically relevant multichromophoric systems, which are similar to the LH2 complex. Computationally, the new method is very tractable, especially for slow processes. We foresee that the method can be applied to efficiently calculate transfer in artificial systems as well and may pave the way for calculating multidimensional spectra of extensive multichromophoric systems in the future.

Hypoxia is a hallmark of the tumor microenvironment (TME) that promotes tumor development and metastasis. Photodynamic therapy (PDT) is a promising strategy in the treatment of tumors, but it is limited by the lack of oxygen in TME. In this work, an O2 self-supply PDT system is constructed by co-encapsulation of chlorin e6 (Ce6) and a MnO2 core in an engineered ferritin (Ftn), generating a nanozyme promoted PDT nanoformula (Ce6/Ftn@MnO2 ) for tumor therapy. Ce6/Ftn@MnO2 exhibits a uniform small size (15.5 nm) and high stability due to the inherent structure of Ftn. The fluorescence imaging and immunofluorescence analysis demonstrate the pronounced accumulation of Ce6/Ftn@MnO2 in the tumors of mice, and the treatment significantly decreases the expression of hypoxia-inducible factor (HIF)-1α. The Ce6/Ftn@MnO2 nanoplatform exerts a more potent anti-tumor efficacy with negligible damage to normal tissues compared to the treatment with free Ce6. Moreover, the weak acidity and the presence of H2 O2 in TME significantly enhances the r1 relativity of Ce6/Ftn@MnO2 , resulting in a prominent enhancement of MRI imaging in the tumor. This bio-mimic Ftn strategy not only improves the in vivo distribution and retention of Ce6, but also enhances the effectiveness and precision of PDT by TME modulation.

为提高基于结构光的高温锻件在线自动化测量的光栅投影与相位计算速度、减小零件热辐射对测量精度的影响、降低环境振动造成的精度损失,提出了一种只需要4幅不同频率光栅投影的、不需要相移的快速相位求解算法.在分析传统多频4步相移的相位求解对高温锻件测量效率与精度影响的基础上,首先,从余弦光栅图像中分离出交流分量,对其利用离散信号的希尔伯特变换代替多步相移来计算相位;然后,根据多频外差法的频率混合得到合成频率的合成相位图;最后,根据相应的反三角函数的值域区间,对合成相位进行平移计算展开相位.仿真结果表明,该方法可以显著提高计算效率,有效减少测量过程停留,降低热辐射和振动对测量精度的影响.现场测量结果表明,该方法可以满足锻件全尺寸数据的测量需求,为锻造过程实时监控、及时消除异常波动、提高和控制锻件精度提供了保障.

Purpose Quantitative T-1 and T-2 mapping in non-human primates with whole-brain coverage is challenged by the requirement of sub-millimeter resolution and the inhomogeneity of the transmit magnetic field (B-1(+)) covering a large field of view. The goal of the current study is to develop a magnetic resonance fingerprinting (MRF) method for simultaneous T-1 and T-2 mapping of the entire macaque brain within feasible scan time. Methods A three-dimensional (3D) MRF sequence with both inversion- and T-2-preparation modules was developed and evaluated on a 9.4 T preclinical scanner. Data acquisition used a 3D stack-of-spirals trajectory, with undersampling along both the in-plane and the through-plane directions. The effect of B-1(+) inhomogeneity was accounted for by matching the acquired fingerprint to a dictionary simulated with the B-1(+) factors measured from a separate scan. In vitro and ex vivo studies were performed to evaluate the accuracy and the undersampling capacity of the MRF method. The application of the MRF method for in vivo, brain-wide T-1 and T-2 mapping was demonstrated on macaques at 4, 6, and 12 years of age. Results The MRF method enabled highly repeatable T-1 and T-2 mapping at high spatial resolution (0.35 x 0.35 x 1 mm(3)) with an acceleration factor of 24. In vivo studies showed significant age-related T-2 reduction in deep gray nuclei including the globus pallidus, the putamen, and the caudate nucleus. Conclusions This study demonstrates the first MRF study for brain-wide, multi-parametric quantification in non-human primates with sub-millimeter resolution.

Challenges remain in precisely diagnosing the progress of liver fibrosis in a noninvasive way. We here synthesized small (4 nm) heterogeneous iron oxide/dysprosium oxide nanoparticles (IO-DyO NPs) as a contrast agent (CA) for magnetic resonance imaging (MRI) to precisely diagnose liver fibrosis in vivo at both 7.0 and 9.4 T field strength. Our IO-DyO NPs can target the liver and show an increased T2 relaxivity along with an increase of magnetic field strength. At a ultrahigh magnetic field, IO-DyO NPs can significantly improve spatial/temporal image resolution and signal-to-noise ratio of the liver and precisely distinguish the early and moderate liver fibrosis stages. Our IO-DyO NP-based MRI diagnosis can exactly match biopsy (a gold standard for liver fibrosis diagnosis in the clinic) but avoid the invasiveness of biopsy. Moreover, our IO-DyO NPs show satisfactory biosafety in vitro and in vivo. This work illustrates an advanced T2 CA used in ultrahigh-field MRI (UHFMRI) for the precise diagnosis of liver fibrosis via a noninvasive means.

培养创新型人才已经成为新时期材料成型及控制工程专业的重要课题之一.以3D打印材料课程为例,以提升新时期材料成型及控制工程专业学生创新能力为中心,开展教学方法改革,提出理论教学、实践教学、科研教学相结合的新型教学方法,为全面提升相关专业人才培养质量提供一些借鉴和参考.

激光粉末床熔融(LPBF)技术具有成形精度高、易于成形复杂结构零件的优点,已广泛应用于医疗、造船、航空航天等领域.然而由于成形过程复杂,激光粉末床熔融零件的缺陷通常难以控制,阻碍了该技术的进一步发展和应用.原位监测技术能够实时获取成形过程中零件的特征信息,为工艺参数的优化指明方向,提升零件的成形质量,是近年来研究的热点之一.总结了最近十年的相关研究成果,以粉末床、熔池及成形层三个监测阶段为线索,对比分析了多种针对激光粉末床熔融成形过程几何特征的光学原位监测技术,包括使用高分辨率工业相机、高速相机、条纹投影等监测方法的优点与不足,简要介绍了监测数据处理过程中用到的图像处理算法及机器学习方法,并展望了激光粉末床熔融原位监测技术的未来发展方向.