Development of Highly Enantio- and Z-Selective Grubbs Catalysts via Controllable C-H Bond Activation.

Asymmetric olefin metathesis is a powerful strategy for stereocontrolled synthesis that allows the formation of chiral elements in conjunction with carbon-carbon double bonds. Here, we report a new series of cyclometalated stereogenic-at-Ru catalysts that enable highly efficient asymmetric ring opening/cross-metathesis (AROCM) and asymmetric ring-closing metathesis (ARCM) reactions. Single enantiomers of these catalysts with either right-handed or left-handed configurations at the Ru center can be easily accessed via highly stereoselective C-H bond activation-based cyclometalation. Right-handed chiral Ru catalysts enabled the Z- and enantioselective AROCM of a wide range of norbornenes and terminal alkenes, generating densely functionalized cyclopentanes with excellent stereo- and enantioselectivities (99:1 Z/E, up to 99% ee). Left-handed chiral Ru catalysts enabled the facile ARCM of sterically unhindered, all-terminal prochiral trienes, which had not been achieved by previous Ru catalysts, providing simple cyclic ethers and amides with tertiary or quaternary carbon stereocenters with excellent enantioselectivities (up to 99% ee).

Effect of exercise intervention on lung function in asthmatic adults: a network meta-analysis.

OBJECTIVE: Exercise has traditionally been viewed as a contraindication for individuals with asthma, as it was believed to trigger or worsen acute asthma attacks. However, with scientific and appropriate exercise training, it has been proven that respiratory function and exercise capacity can be effectively improved and enhanced in asthma patients. This study aimed to compare the effects of different types of exercise on pulmonary function in adult patients with asthma using Network Meta-analysis. METHODS: We conducted a comprehensive search of electronic databases such as PubMed, Cochrane Library, Web of Science, CNKI for randomized controlled trials (RCTs) that investigated the effects of exercise on lung function in adult patients with asthma from inception to February 2023. Information including on first author, publication time, total sample size, intervention period, interventions, and outcome indicators were collected, and relevant statistical analyses were performed using Stata 17.0 software and Revman 5.4. RESULTS: A total of 28 randomized controlled trials with 2,155 patients with asthma were finally included. The results of Network Meta-analysis showed that compared with control group, breathing training (BT)、aerobic training (AT)、relaxation training (RT)、yoga training (YG) and breathing combined with aerobic training (BT + AT) improved Forced Expiratory Volume in the first second (FEV1) levels; AT、BT、YG and BT + AT improved the level of Forced Vital Capacity (FVC); BT、AT、RT、YG and BT + AT improved Peak Expiratory Flow (PEF); BT、AT、and YG improved Forced Expiratory Volume in the first second/Forced Vital Capacity (FEV1/FVC).The results of SUCRA probability ranking showed that RT had the most significant effect on improving the FEV1[SMD = 1.13,95%CI(0.83,1.43), p<0.001] levels, BT + AT had the most significant effect on improving the FVC[SMD = 0.71,95%CI(0.47,0.95), p<0.001] level; YG had the most significant effect on improving the PEF[SMD = 0.79,95%CI(0.55,1.02), p<0.001] level. CONCLUSIONS: BT + AT and YG may be more advantageous in improving lung function in adult asthmatics.

Optical Flow-Based Full-Field Quantitative Blood-Flow Velocimetry Using Temporal Direction Filtering and Peak Interpolation.

The quantitative measurement of the microvascular blood-flow velocity is critical to the early diagnosis of microvascular dysfunction, yet there are several challenges with the current quantitative flow velocity imaging techniques for the microvasculature. Optical flow analysis allows for the quantitative imaging of the blood-flow velocity with a high spatial resolution, using the variation in pixel brightness between consecutive frames to trace the motion of red blood cells. However, the traditional optical flow algorithm usually suffers from strong noise from the background tissue, and a significant underestimation of the blood-flow speed in blood vessels, due to the errors in detecting the feature points in optical images. Here, we propose a temporal direction filtering and peak interpolation optical flow method (TPIOF) to suppress the background noise, and improve the accuracy of the blood-flow velocity estimation. In vitro phantom experiments and in vivo animal experiments were performed to validate the improvements in our new method.

Epidemiological and clinical features of hypertrophic scar and keloid in Chinese college students: A university-based cross-sectional survey.

Background: Hypertrophic scar (HS) and keloid (KD) are common dermal fibroproliferative growth caused by pathological wound healing. HS's prevalence is currently undetermined in China. Though it primarily occurs in dark-skinned individuals, KD can develop in all races, and its prevalence among Chinese people is poorly documented. Objective: To explore the present epidemiological status of them in Chinese college students. Methods: We conducted a university-based cross-sectional study at one university in Fujian, China. A total of 1785 participants aged 16-34 years (mean age, 20.0 ± 2.0; 58.7% female) were enrolled and statistical analyses were performed. Results: HS and KD were observed in 5.2% (95% confidence interval [CI]: 4.2-6.2) and 0.6% (95% CI: 0.3-1.0) of the population respectively. There was a significant difference by sex in HS (P < 0.05), but not in KD. The prevalence of HS and KD both showed a significant difference by age (P < 0.05), but not in ethnic and native place distribution. The occurrence of HS and KD were both concentrated in individuals 9-20 years old (HS: 77.2%; KD: 81.8%). They were mainly distributed in the upper limbs (52.1%; 64.3%), and the main cause was trauma (51.0%; 35.7%). In addition, male sex was a risk factor for HS (adjusted P < 0.001), and KD was associated with age ≥22 years and family history (adjusted P < 0.050). Conclusion: HS and KD are common in Chinese college students, and more attention and research is warranted.

Unifying temporal phase unwrapping framework using deep learning.

Temporal phase unwrapping (TPU) is significant for recovering an unambiguous phase of discontinuous surfaces or spatially isolated objects in fringe projection profilometry. Generally, temporal phase unwrapping algorithms can be classified into three groups: the multi-frequency (hierarchical) approach, the multi-wavelength (heterodyne) approach, and the number-theoretic approach. For all of them, extra fringe patterns of different spatial frequencies are required for retrieving the absolute phase. Due to the influence of image noise, people have to use many auxiliary patterns for high-accuracy phase unwrapping. Consequently, image noise limits the efficiency and the measurement speed greatly. Further, these three groups of TPU algorithms have their own theories and are usually applied in different ways. In this work, for the first time to our knowledge, we show that a generalized framework using deep learning can be developed to perform the TPU task for different groups of TPU algorithms. Experimental results show that benefiting from the assistance of deep learning the proposed framework can mitigate the impact of noise effectively and enhance the phase unwrapping reliability significantly without increasing the number of auxiliary patterns for different TPU approaches. We believe that the proposed method demonstrates great potential for developing powerful and reliable phase retrieval techniques.

Structural Transformation in a Sulfurized Polymer Cathode to Enable Long-Life Rechargeable Lithium-Sulfur Batteries.

Sulfurized polyacrylonitrile (SPAN) represents a class of sulfur-bonded polymers, which have shown thousands of stable cycles as a cathode in lithium-sulfur batteries. However, the exact molecular structure and its electrochemical reaction mechanism remain unclear. Most significantly, SPAN shows an over 25% 1st cycle irreversible capacity loss before exhibiting perfect reversibility for subsequent cycles. Here, with a SPAN thin-film platform and an array of analytical tools, we show that the SPAN capacity loss is associated with intramolecular dehydrogenation along with the loss of sulfur. This results in an increase in the aromaticity of the structure, which is corroborated by a >100× increase in electronic conductivity. We also discovered that the conductive carbon additive in the cathode is instrumental in driving the reaction to completion. Based on the proposed mechanism, we have developed a synthesis procedure to eliminate more than 50% of the irreversible capacity loss. Our insights into the reaction mechanism provide a blueprint for the design of high-performance sulfurized polymer cathode materials.

Understanding of protomers/deprotomers by combining mass spectrometry and computation.

Multifunctional compounds may form different prototropic isomers under different conditions, which are known as protomers/deprotomers. In biological systems, these protomer/deprotomer isomers affect the interaction modes and conformational landscape between compounds and enzymes and thus present different biological activities. Study on protomers/deprotomers is essentially the study on the acidity/basicity of each intramolecular functional group and its effect on molecular structure. In recent years, the combination of mass spectrometry (MS) and computational chemistry has been proven to be a powerful and effective means to study prototropic isomers. MS-based technologies are developed to discriminate and characterize protomers/deprotomers to provide structural information and monitor transformations, showing great superiority than other experimental methods. Computational chemistry is used to predict the thermodynamic stability of protomers/deprotomers, provide the simulated MS/MS spectra, infrared spectra, and calculate collision cross-section values. By comparing the theoretical data with the corresponding experimental results, the researchers can not only determine the protomer/deprotomer structure, but also investigate the structure-activity relationship in a given system. This review covers various MS methods and theoretical calculations and their devotion to isomer discrimination, structure identification, conformational transformation, and phase transition investigation of protomers/deprotomers.

Cavity soliton in a cyclic polarization permutation fiber resonator.

Cavity solitons are shape-preserving waveforms infinitely revolving around a cavity, which have numerous applications from spectroscopy to telecommunications. Although the cavity solitons have been widely studied for their special time-frequency characteristics over the past decade, the spectral flatness will be a large limitation in some applications without any extra filtering process. In this paper, we report on the generation of a distinct cavity soliton in a cyclic polarization permutation fiber resonator. With the simultaneous excitation of two orthogonal polarization modes with equally opposite dispersion, vectorial cavity solitons possessing broader and flatter spectra can be generated. In order to verify the concept, a numerical model of the polarization-maintaining fiber is proposed and the soliton with a flattened spectrum can be formed. The results enrich the soliton dynamics in the vectorial dissipation system.

Polarization-decoupled cavity solitons generation in Kerr resonators with flattened near-zero dispersion.

Two frequency combs emitting from a single cavity are of great potential in the field of dual-comb spectroscopy because they are mutually coherent and therefore the common mode noise can be suppressed naturally. However, it is difficult to fully and flexibly control the repetition frequency difference in most of the all-optical schemes. In this paper, a birefringence-compensation Kerr resonator is proposed for the mutual dual-comb generation. It is shown that by offset aligning the fast and slow axis with appropriate fiber length, the total birefringence of the cavity can be equalized while the local one keeps at a high level. Theoretical investigations reveal that the polarization decoupled mutual dual-comb can be generated with nearly the same power level and arbitrary repetition frequency difference. Additionally, a numerical model of polarization-maintaining fiber (PMF) with near-zero dispersion is proposed for the proof of the concept. Based on this fiber, the coherent polarization-decoupled dual-comb with 10-dB bandwidth of 33 nm can be obtained. And the repetition frequency difference can be flexibly tuned compared to the cavity without offset alignment.

Deep Learning-Based 3D Measurements with Near-Infrared Fringe Projection.

Fringe projection profilometry (FPP) is widely applied to 3D measurements, owing to its advantages of high accuracy, non-contact, and full-field scanning. Compared with most FPP systems that project visible patterns, invisible fringe patterns in the spectra of near-infrared demonstrate fewer impacts on human eyes or on scenes where bright illumination may be avoided. However, the invisible patterns, which are generated by a near-infrared laser, are usually captured with severe speckle noise, resulting in 3D reconstructions of limited quality. To cope with this issue, we propose a deep learning-based framework that can remove the effect of the speckle noise and improve the precision of the 3D reconstruction. The framework consists of two deep neural networks where one learns to produce a clean fringe pattern and the other to obtain an accurate phase from the pattern. Compared with traditional denoising methods that depend on complex physical models, the proposed learning-based method is much faster. The experimental results show that the measurement accuracy can be increased effectively by the presented method.

Composite fringe projection deep learning profilometry for single-shot absolute 3D shape measurement.

Single-shot fringe projection profilometry (FPP) is essential for retrieving the absolute depth information of the objects in high-speed dynamic scenes. High-precision 3D reconstruction using only one single pattern has become the ultimate goal in FPP. The frequency-multiplexing (FM) method is a promising strategy for realizing single-shot absolute 3D measurement by compounding multi-frequency fringe information for phase unwrapping. In order to solve the problem of serious spectrum aliasing caused by multiplexing schemes that cannot be removed by traditional spectrum analysis algorithms, we apply deep learning to frequency multiplexing composite fringe projection and propose a composite fringe projection deep learning profilometry (CDLP). By combining physical model and data-driven approaches, we demonstrate that the model generated by training an improved deep convolutional neural network can directly perform high-precision and unambiguous phase retrieval on a single-shot spatial frequency multiplexing composite fringe image. Experiments on both static and dynamic scenes demonstrate that our method can retrieve robust and unambiguous phases information while avoiding spectrum aliasing and reconstruct high-quality absolute 3D surfaces of objects only by projecting a single composite fringe image.

Deep learning in optical metrology: a review.

With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, nondestructive testing, experimental mechanics, and biomedicine. In recent years, deep learning, a subfield of machine learning, is emerging as a powerful tool to address problems by learning from data, largely driven by the availability of massive datasets, enhanced computational power, fast data storage, and novel training algorithms for the deep neural network. It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology. Unlike the traditional "physics-based" approach, deep-learning-enabled optical metrology is a kind of "data-driven" approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances. In this review, we present an overview of the current status and the latest progress of deep-learning technologies in the field of optical metrology. We first briefly introduce both traditional image-processing algorithms in optical metrology and the basic concepts of deep learning, followed by a comprehensive review of its applications in various optical metrology tasks, such as fringe denoising, phase retrieval, phase unwrapping, subset correlation, and error compensation. The open challenges faced by the current deep-learning approach in optical metrology are then discussed. Finally, the directions for future research are outlined.

Single-shot 3D shape measurement using an end-to-end stereo matching network for speckle projection profilometry.

Speckle projection profilometry (SPP), which establishes the global correspondences between stereo images by projecting only a single speckle pattern, has the advantage of single-shot 3D reconstruction. Nevertheless, SPP suffers from the low matching accuracy of traditional stereo matching algorithms, which fundamentally limits its 3D measurement accuracy. In this work, we propose a single-shot 3D shape measurement method using an end-to-end stereo matching network for SPP. To build a high-quality SPP dataset for training the network, by combining phase-shifting profilometry (PSP) and temporal phase unwrapping techniques, high-precision absolute phase maps can be obtained to generate accurate and dense disparity maps with high completeness as the ground truth by phase matching. For the architecture of the network, a multi-scale residual subnetwork is first leveraged to synchronously extract compact feature tensors with 1/4 resolution from speckle images for constructing the 4D cost volume. Considering that the cost filtering based on 3D convolution is computationally costly, a lightweight 3D U-net network is proposed to implement efficient 4D cost aggregation. In addition, because the disparity maps in the SPP dataset should have valid values only in the foreground, a simple and fast saliency detection network is integrated to avoid predicting the invalid pixels in the occlusions and background regions, thereby implicitly enhancing the matching accuracy for valid pixels. Experiment results demonstrated that the proposed method improves the matching accuracy by about 50% significantly compared with traditional stereo matching methods. Consequently, our method achieves fast and absolute 3D shape measurement with an accuracy of about 100µm through a single speckle pattern.

Collimating micro-lens fiber array for noncontact near-infrared diffuse correlation tomography.

Near-infrared diffuse correlation spectroscopy/tomography (DCS/DCT) has recently emerged as a noninvasive measurement/imaging technology for tissue blood flow. In DCT studies, the high-dense collection of light temporal autocorrelation curves (g 2(τ)) via fiber array are critical for image reconstruction of blood flow. Previously, the camera-based fiber array limits the field of view (FOV), precluding its applications on large-size human tissues. The line-shape fiber probe based on lens combination, which is predominantly used in current DCT studies, requires rotated-scanning over the surface of target tissue, substantially prolonging the measurement time and increasing the system instability. In this study, we design a noncontact optical probe for DCT based on collimating micro-lens fiber array, termed as FA-nc-DCT system. For each source/detector fiber, a single optical path was collimated by coupling with one micro-lens in the fiber array that is integrated in a square-shape base. Additionally, an 8×8 optical switch is used to share the hardware laser and detectors without spatial scanning. The FA-nc approach for the precise collection of g 2(τ) curves was validated through a speed-varied phantom experiment and the human experiments of cuff occlusion, from which the expected value of the blood flow index (BFI) was obtained. Furthermore, the flow anomaly in the phantom and the ischemic muscle in human were accurately reconstructed from the FA-nc-DCT system, which is combined with the imaging framework based on the Nth-order linear algorithm that we recently created. Those outcomes demonstrated the great potential of FA-nc-DCT technology for fast and robust imaging of various diseases such as human breast cancers.

Efficient photoactivatable Dre recombinase for cell type-specific spatiotemporal control of genome engineering in the mouse.

Precise genetic engineering in specific cell types within an intact organism is intriguing yet challenging, especially in a spatiotemporal manner without the interference caused by chemical inducers. Here we engineered a photoactivatable Dre recombinase based on the identification of an optimal split site and demonstrated that it efficiently regulated transgene expression in mouse tissues spatiotemporally upon blue light illumination. Moreover, through a double-floxed inverted open reading frame strategy, we developed a Cre-activated light-inducible Dre (CALID) system. Taking advantage of well-defined cell-type-specific promoters or a well-established Cre transgenic mouse strain, we demonstrated that the CALID system was able to activate endogenous reporter expression for either bulk or sparse labeling of CaMKIIα-positive excitatory neurons and parvalbumin interneurons in the brain. This flexible and tunable system could be a powerful tool for the dissection and modulation of developmental and genetic complexity in a wide range of biological systems.

Real-time high dynamic range 3D measurement using fringe projection.

Fringe projection profilometry (FPP) is a widely used technique for real-time three-dimensional (3D) shape measurement. However, it tends to compromise when measuring objects that have a large variation range of surface reflectivity. In this paper, we present a FPP method that can increase the dynamic range for real-time 3D measurements. First, binary fringe patterns are projected to generate grayscale sinusoidal patterns with the defocusing technique. Each pattern is then captured twice with different exposure values in one projection period. With image fusion, surfaces under appropriate exposure are retained. To improve the real-time performance of high dynamic range (HDR) 3D shape measurements, we build a binocular fringe projection profilometry system that saves the number of patterns by geometry constraint. Further, to ensure the accuracy and robustness of HDR 3D measurements, we propose a mixed phase unwrapping method that can reduce phase unwrapping errors for dense fringe patterns. Experiment results show that the proposed method can realize accurate and real-time 3D measurement for HDR scenes at 28 frames per second.

Single-shot absolute 3D shape measurement with deep-learning-based color fringe projection profilometry.

Recovering the high-resolution three-dimensional (3D) surface of an object from a single frame image has been the ultimate goal long pursued in fringe projection profilometry (FPP). The color fringe projection method is one of the technologies with the most potential towards such a goal due to its three-channel multiplexing properties. However, the associated color imbalance, crosstalk problems, and compromised coding strategy remain major obstacles to overcome. Inspired by recent successes of deep learning for FPP, we propose a single-shot absolute 3D shape measurement with deep-learning-based color FPP. Through "learning" on extensive data sets, the properly trained neural network can "predict" the high-resolution, motion-artifact-free, crosstalk-free absolute phase directly from one single color fringe image. Compared with the traditional approach, our method allows for more accurate phase retrieval and more robust phase unwrapping. Experimental results demonstrate that the proposed approach can provide high-accuracy single-frame absolute 3D shape measurement for complicated objects.

Calibration method for panoramic 3D shape measurement with plane mirrors.

High-speed panoramic three-dimensional (3D) shape measurement can be achieved by introducing plane mirrors into the traditional fringe projection profilometry (FPP) system because such a system simultaneously captures fringe patterns from three different perspectives (i.e., by a real camera and two virtual cameras in the plane mirrors). However, calibrating such a system is nontrivial due to the complicated setup. This work introduces a flexible new technique to calibrate such a system. We first present the mathematical representation of the plane mirror, and then mathematically prove that it only requires the camera to observe a set of feature point pairs (including real points and virtual points) to generate a solution to the reflection matrix of a plane mirror. By calibrating the virtual and real camera in the same world coordinate system, 3D point cloud data obtained from real and virtual perspectives can be automatically aligned to generate a panoramic 3D model of the object. Finally, we developed a system to verify the performance of the proposed calibration technique for panoramic 3D shape measurement.

Temporal phase unwrapping using deep learning.

The multi-frequency temporal phase unwrapping (MF-TPU) method, as a classical phase unwrapping algorithm for fringe projection techniques, has the ability to eliminate the phase ambiguities even while measuring spatially isolated scenes or the objects with discontinuous surfaces. For the simplest and most efficient case in MF-TPU, two groups of phase-shifting fringe patterns with different frequencies are used: the high-frequency one is applied for 3D reconstruction of the tested object and the unit-frequency one is used to assist phase unwrapping for the wrapped phase with high frequency. The final measurement precision or sensitivity is determined by the number of fringes used within the high-frequency pattern, under the precondition that its absolute phase can be successfully recovered without any fringe order errors. However, due to the non-negligible noises and other error sources in actual measurement, the frequency of the high-frequency fringes is generally restricted to about 16, resulting in limited measurement accuracy. On the other hand, using additional intermediate sets of fringe patterns can unwrap the phase with higher frequency, but at the expense of a prolonged pattern sequence. With recent developments and advancements of machine learning for computer vision and computational imaging, it can be demonstrated in this work that deep learning techniques can automatically realize TPU through supervised learning, as called deep learning-based temporal phase unwrapping (DL-TPU), which can substantially improve the unwrapping reliability compared with MF-TPU even under different types of error sources, e.g., intensity noise, low fringe modulation, projector nonlinearity, and motion artifacts. Furthermore, as far as we know, our method was demonstrated experimentally that the high-frequency phase with 64 periods can be directly and reliably unwrapped from one unit-frequency phase using DL-TPU. These results highlight that challenging issues in optical metrology can be potentially overcome through machine learning, opening new avenues to design powerful and extremely accurate high-speed 3D imaging systems ubiquitous in nowadays science, industry, and multimedia.