Cep57 regulates human centrosomes through multivalent interactions.

Human Cep57 is a coiled-coil scaffold at the pericentriolar matrix (PCM), controlling centriole duplication and centrosome maturation for faithful cell division. Genetic truncation mutations of Cep57 are associated with the mosaic-variegated aneuploidy (MVA) syndrome. During interphase, Cep57 forms a complex with Cep63 and Cep152, serving as regulators for centrosome maturation. However, the molecular interplay of Cep57 with these essential scaffolding proteins remains unclear. Here, we demonstrate that Cep57 undergoes liquid-liquid phase separation (LLPS) driven by three critical domains (NTD, CTD, and polybasic LMN). In vitro Cep57 condensates catalyze microtubule nucleation via the LMN motif-mediated tubulin concentration. In cells, the LMN motif is required for centrosomal microtubule aster formation. Moreover, Cep63 restricts Cep57 assembly, expansion, and microtubule polymerization activity. Overexpression of competitive constructs for multivalent interactions, including an MVA mutation, leads to excessive centrosome duplication. In Cep57-depleted cells, self-assembly mutants failed to rescue centriole disengagement and PCM disorganization. Thus, Cep57's multivalent interactions are pivotal for maintaining the accurate structural and functional integrity of human centrosomes.

Reliable image dehazing by NeRF.

Image dehazing is a typical low-level visual task. With the continuous improvement of network performance and the introduction of various prior knowledge, the ability of image dehazing is becoming stronger. However, the existing dehazing methods have problems such as the inability to obtain real shooting datasets, unreliable dehazing processes, and the difficulty to deal with complex lighting scenes. To solve these problems, we propose a new haze model combining the optical scattering model and the computer graphics rendering. Based on the new haze model, we propose a high-quality and widely applicable dehazing dataset generation pipeline that does not require paired-data training and prior knowledge. We reconstruct the three-dimensional fog space with array camera and remove haze by thresholding voxel deletion. We use the Unreal Engine 5 to generate simulation datasets and the real shooting in laboratory to verify the effectiveness and the reliability of our generation pipeline. Through our pipeline, we can obtain wonderful dehaze results and dehaze datasets under various complex outdoors lighting conditions. We also propose a dehaze dataset enhancement method based on voxel control. Our pipeline and data enhancement are suitable for the latest algorithm model, these solutions can obtain better visual effects and objective indicators.

Wavelength encoding spectral imaging based on the combination of deeply learned filters and an RGB camera.

Hyperspectral imaging is a critical tool for gathering spatial-spectral information in various scientific research fields. As a result of improvements in spectral reconstruction algorithms, significant progress has been made in reconstructing hyperspectral images from commonly acquired RGB images. However, due to the limited input, reconstructing spectral information from RGB images is ill-posed. Furthermore, conventional camera color filter arrays (CFA) are designed for human perception and are not optimal for spectral reconstruction. To increase the diversity of wavelength encoding, we propose to place broadband encoding filters in front of the RGB camera. In this condition, the spectral sensitivity of the imaging system is determined by the filters and the camera itself. To achieve an optimal encoding scheme, we use an end-to-end optimization framework to automatically design the filters' transmittance functions and optimize the weights of the spectral reconstruction network. Simulation experiments show that our proposed spectral reconstruction network has excellent spectral mapping capabilities. Additionally, our novel joint wavelength encoding imaging framework is superior to traditional RGB imaging systems. We develop the deeply learned filter and conduct actual shooting experiments. The spectral reconstruction results have an attractive spatial resolution and spectral accuracy.

Trilaciclib dosage in Chinese patients with extensive-stage small cell lung cancer: a pooled pharmacometrics analysis.

This study aimed to analyze potential ethnic disparities in the dose-exposure-response relationships of trilaciclib, a first-in-class intravenous cyclin-dependent kinase 4/6 inhibitor for treating chemotherapy-induced myelosuppression in patients with extensive-stage small cell lung cancer (ES-SCLC). This investigation focused on characterizing these relationships in both Chinese and non-Chinese patients to further refine the dosing regimen for trilaciclib in Chinese patients with ES-SCLC. Population pharmacokinetic (PopPK) and exposure-response (E-R) analyses were conducted using pooled data from four randomized phase 2/3 trials involving Chinese and non-Chinese patients with ES-SCLC. PopPK analysis revealed that trilaciclib clearance in Chinese patients was approximately 17% higher than that in non-Chinese patients with ES-SCLC. Sex and body surface area influenced trilaciclib pharmacokinetics in both populations but did not exert a significant clinical impact. E-R analysis demonstrated that trilaciclib exposure increased with a dosage escalation from 200 to 280 mg/m2, without notable changes in myeloprotective or antitumor efficacy. However, the incidence of infusion site reactions, headaches, and phlebitis/thrombophlebitis rose with increasing trilaciclib exposure in both Chinese and non-Chinese patients with ES-SCLC. These findings suggest no substantial ethnic disparities in the dose-exposure-response relationship between Chinese and non-Chinese patients. They support the adoption of a 240-mg/m2 intravenous 3-day or 5-day dosing regimen for trilaciclib in Chinese patients with ES-SCLC.

Elimination of varying chromatic aberrations based on diffractive optics.

In telescopic systems consisting of Alvarez lenses, chromatic aberrations vary with the magnifications and the fields of view. Computational imaging has developed rapidly in recent years, therefore we propose a method of optimizing the DOE and the post-processing neural network in 2 stages for achromatic aberrations. We apply the iterative algorithm and the gradient descent method to optimize the DOE, respectively, and then adopt U-Net to further optimize the results. The results show that the optimized DOEs improve the results, the gradient descent optimized DOE with U-Net performs the best and has a very robust and good performance in the case of simulated chromatic aberrations. The results also verify the validity of our algorithm.

Snapshot hyperspectral imaging based on equalization designed DOE.

Hyperspectral imaging attempts to determine distinctive information in spatial and spectral domain of a target. Over the past few years, hyperspectral imaging systems have developed towards lighter and faster. In phase-coded hyperspectral imaging systems, a better coding aperture design can improve the spectral accuracy relatively. Using wave optics, we post an equalization designed phase-coded aperture to achieve desired equalization point spread functions (PSFs) which provides richer features for subsequent image reconstruction. During the reconstruction of images, our raised hyperspectral reconstruction network, CAFormer, achieves better results than the state-of-the-art networks with less computation by substituting self-attention with channel-attention. Our work revolves around the equalization design of the phase-coded aperture and optimizes the imaging process from three aspects: hardware design, reconstruction algorithm, and PSF calibration. Our work is putting snapshot compact hyperspectral technology closer to a practical application.

Image restoration for optical zooming system based on Alvarez lenses.

Alvarez lenses are known for their ability to achieve a broad range of optical power adjustment by utilizing complementary freeform surfaces. However, these lenses suffer from optical aberrations, which restrict their potential applications. To address this issue, we propose a field of view (FOV) attention image restoration model for continuous zooming. In order to simulate the degradation of optical zooming systems based on Alvarez lenses (OZA), a baseline OZA is designed where the polynomial for the Alvarez lenses consists of only three coefficients. By computing spatially varying point spread functions (PSFs), we simulate the degraded images of multiple zoom configurations and conduct restoration experiments. The results demonstrate that our approach surpasses the compared methods in the restoration of degraded images across various zoom configurations while also exhibiting strong generalization capabilities under untrained configurations.

ASF-Transformer: neutralizing the impact of atmospheric turbulence on optical imaging through alternating learning in the spatial and frequency domains.

Atmospheric turbulence, a pervasive and complex physical phenomenon, challenges optical imaging across various applications. This paper presents the Alternating Spatial-Frequency (ASF)-Transformer, a learning-based method for neutralizing the impact of atmospheric turbulence on optical imaging. Drawing inspiration from split-step propagation and correlated imaging principles, we propose the Alternating Learning in Spatial and Frequency domains (LASF) mechanism. This mechanism utilizes two specially designed transformer blocks that alternate between the spatial and Fourier domains. Assisted by the proposed patch FFT loss, our model can enhance the recovery of intricate textures without the need for generative adversarial networks (GANs). Evaluated across diverse test mediums, our model demonstrated state-of-the-art performance in comparison to recent methods. The ASF-Transformer diverges from mainstream GAN-based solutions, offering a new strategy to combat image degradation introduced by atmospheric turbulence. Additionally, this work provides insights into neural network architecture by integrating principles from optical theory, paving the way for innovative neural network designs in the future.

Dark2Light: multi-stage progressive learning model for low-light image enhancement.

Due to severe noise and extremely low illuminance, restoring from low-light images to normal-light images remains challenging. Unpredictable noise can tangle the weak signals, making it difficult for models to learn signals from low-light images, while simply restoring the illumination can lead to noise amplification. To address this dilemma, we propose a multi-stage model that can progressively restore normal-light images from low-light images, namely Dark2Light. Within each stage, We divide the low-light image enhancement (LLIE) into two main problems: (1) illumination enhancement and (2) noise removal. Firstly, we convert the image space from sRGB to linear RGB to ensure that illumination enhancement is approximately linear, and design a contextual transformer block to conduct illumination enhancement in a coarse-to-fine manner. Secondly, a U-Net shaped denoising block is adopted for noise removal. Lastly, we design a dual-supervised attention block to facilitate progressive restoration and feature transfer. Extensive experimental results demonstrate that the proposed Dark2Light outperforms the state-of-the-art LLIE methods both quantitatively and qualitatively.

Direct distortion prediction method for AR-HUD dynamic distortion correction.

Dynamic distortion is one of the most critical factors affecting the experience of automotive augmented reality head-up displays (AR-HUDs). A wide range of views and the extensive display area result in extraordinarily complex distortions. Existing methods based on the neural network first obtain distorted images and then get the predistorted data for training mostly. This paper proposes a distortion prediction framework based on the neural network. It directly trains the network with the distorted data, realizing dynamic adaptation for AR-HUD distortion correction and avoiding errors in coordinate interpolation. Additionally, we predict the distortion offsets instead of the distortion coordinates and present a field of view (FOV)-weighted loss function based on the spatial-variance characteristic to further improve the prediction accuracy of distortion. Experiments show that our methods improve the prediction accuracy of AR-HUD dynamic distortion without increasing the network complexity or data processing overhead.

Design of an optimized Alvarez lens based on the fifth-order polynomial combination.

This paper proposes an optimized design of the Alvarez lens by utilizing a combination of three fifth-order X-Y polynomials. It can effectively minimize the curvature of the lens surface to meet the manufacturing requirements. The phase modulation function and aberration of the proposed lens are evaluated by using first-order optical analysis. Simulations compare the proposed lens with the traditional Alvarez lens in terms of surface curvature, zoom capability, and imaging quality. The results demonstrate the exceptional performance of the proposed lens, achieving a remarkable 26.36% reduction in the maximum curvature of the Alvarez lens (with a coefficient A value of 4×10-4 and a diameter of 26 mm) while preserving its original zoom capability and imaging quality.

Non-blind optical degradation correction via frequency self-adaptive and finetune tactics.

In mobile photography applications, limited volume constraints the diversity of optical design. In addition to the narrow space, the deviations introduced in mass production cause random bias to the real camera. In consequence, these factors introduce spatially varying aberration and stochastic degradation into the physical formation of an image. Many existing methods obtain excellent performance on one specific device but are not able to quickly adapt to mass production. To address this issue, we propose a frequency self-adaptive model to restore realistic features of the latent image. The restoration is mainly performed in the Fourier domain and two attention mechanisms are introduced to match the feature between Fourier and spatial domain. Our method applies a lightweight network, without requiring modification when the fields of view (FoV) changes. Considering the manufacturing deviations of a specific camera, we first pre-train a simulation-based model, then finetune it with additional manufacturing error, which greatly decreases the time and computational overhead consumption in implementation. Extensive results verify the promising applications of our technique for being integrated with the existing post-processing systems.

Fast method for designing broadband achromatic diffractive optical elements.

Diffractive optical elements play a crucial role in the miniaturization of the optical systems, especially in correcting achromatic aberration. Considering the rapidity and validity of the design method, we propose a fast method for designing broadband achromatic diffractive optical elements. Based on the direct binary search algorithm, some improvements have been made including the selection of the initial height map to mitigate the uncertainty, the reduction of the variations to accelerate the optimization and the increase of sampling rate to deal with the large operation bandwidth. The initial height map is calculated instead of random initial value. Due to different regions of the height map contributing to point spread functions differently, the variations are reduced to speed up the optimization. The large operation bandwidth is solved by increasing the sampling rate at unfitted wavelengths instead of setting weighting coefficients. We demonstrate via simulations that our method is effective through several examples. The design of broadband achromatic diffractive optical elements can be quickly achieved by our method.

Deep learning-based image reconstruction for photonic integrated interferometric imaging.

Photonic integrated interferometric imaging (PIII) is an emerging technique that uses far-field spatial coherence measurements to extract intensity information from a source to form an image. At present, low sampling rate and noise disturbance are the main factors hindering the development of this technology. This paper implements a deep learning-based method to improve image quality. Firstly, we propose a frequency-domain dataset generation method based on imaging principles. Secondly, spatial-frequency dual-domain fusion networks (SFDF-Nets) are presented for image reconstruction. We utilize normalized amplitude and phase to train networks, which reduces the difficulty of network training using complex data. SFDF-Nets can fuse multi-frame data captured by rotation sampling to increase the sampling rate and generate high-quality spatial images through dual-domain supervised learning and frequency domain fusion. Furthermore, we propose an inverse fast Fourier transform loss (IFFT loss) for network training in the frequency domain. Extensive experiments show that our method improves PSNR and SSIM by 5.64 dB and 0.20, respectively. Our method effectively improves the reconstructed image quality and opens a new dimension in interferometric imaging.

The role of NOTCH2NLC in Parkinson's disease: A clinical, neuroimaging, and pathological study.

BACKGROUND AND PURPOSE: Recently, the pathogenic and intermediate GGC repeat expansion in NOTCH2NLC was detected in Parkinson's disease (PD). However, detailed clinical, neuroimaging, and pathological information of clinically diagnosed PD patients with pathogenic GGC repeat expansion in NOTCH2NLC remains scarce. Thus, we aimed to elucidate the clinical, neuroimaging, and pathological characteristics of PD patients carrying the pathogenic GGC repeat expansion in NOTCH2NLC. METHODS: The NOTCH2NLC GGC repeat expansion was screened in 941 sporadic PD patients and 244 unrelated probands. Comprehensive assessments were performed in three PD patients with pathogenic GGC repeat expansion in NOTCH2NLC. The repeat expansion length was estimated using CRISPR/Cas9-based targeted long-read sequencing. RESULTS: The three patients (two PD patients from Family 1 and one sporadic PD) carrying the pathogenic NOTCH2NLC expansion were reconfirmed with a diagnosis of clinically established PD. Although they lacked the typical neuronal intranuclear inclusion disease (NIID) magnetic resonance imaging (MRI) feature, the typical PD pattern of striatal dopamine transporter loss was detected. Notably, all three patients presented with systemic areflexia, and other secondary causes of polyneuropathy were excluded. Skin biopsy showed intranuclear inclusions and an absence of phosphorylated alpha-synuclein deposition in the skin nerve fibers of all three patients. CONCLUSIONS: Although these clinically diagnosed PD patients with pathogenic GGC repeat expansion in NOTCH2NLC were hardly distinguishable from idiopathic PD based on clinical course and neuroimaging features, the pathological findings indicated that their phenotype was a PD phenocopy of NIID. Systemic areflexia may be an important and unique clinical clue suggesting further genetic testing and skin biopsy examination to confirm the diagnosis of NIID in patients presenting with a PD phenocopy.

Parametric population pharmacokinetics of linezolid: A systematic review.

AIMS: Linezolid is often used for the infections caused by drug-resistant Gram-positive bacteria. Recent studies suggest that large between-subject variability (BSV) and within-subject variability could alter drug pharmacokinetics (PK) during linezolid therapy due to pathophysiological changes. This review synthesized information on linezolid population PK studies and summarized the significant covariates that influence linezolid PK. METHODS: A literature search was performed using PubMed, Web of Science and Embase from their inception to 30 September 2021. Published studies were included if they contained data analysing linezolid PK parameters in humans using a population approach with a nonlinear mixed-effects model. RESULTS: Twenty-five studies conducted in adults and five in paediatrics were included. One- and two-compartment models were the commonly used structural models for linezolid. Body size (weight, lean body weight and body surface area), creatinine clearance (CLcr) and age significantly influenced linezolid PK. The median clearance (CL) values (ranges) in infants (0.128 L/h/kg [0.121-0.135]] and children (0.107 L/h/kg [0.088-0.151]] were higher than in adults (0.098 L/h/kg [0.044-0.237]]. For patients with severe renal impairment (CLcr ≤ 30 mL/min), the CL was 37.2% (15.2-55.3%) lower than in patients with normal renal function. CONCLUSION: The optimal linezolid dosage should be adjusted based on the patient's body size, renal function and age. More studies are needed to explore the exact mechanism of linezolid elimination and evaluate the PK characteristics in paediatric patients.

A heterozygous de novo PSEN1 mutation in a patient with early-onset parkinsonism.

BACKGROUND: Mutations in presenilin 1 (PSEN1) are the most common known genetic cause of early-onset Alzheimer's disease. Patients with PSEN1 mutations exhibit broad phenotypes. Here, we report clinical, neuroimaging and genetic findings in a patient with a de novo mutation in PSEN1 (c.697A > G, p.M233V) presenting with early-onset parkinsonism as the initial and primary symptom. METHODS: We recruited a family with one affected patient with early-onset parkinsonism. The patient underwent comprehensive neurological examination and imaging evaluation. Whole genome sequencing was performed for the proband. RESULTS: The patient presented with parkinsonism and mild cognitive impairment. He had a good response to levodopa. Brain MRI evaluation showed atrophy of the bilateral frontotemporal lobe and hippocampus. 18F-fluorodeoxyglucose-positron emission tomography (PET) and 11C-2ß-carbomethoxy-3ß-(4-fluorophenyl) tropane-PET showed decreased metabolism and dopamine transporter distribution in the bilateral putamen and caudate nucleus. 11C-Pittsburgh compound B -PET showed ß-amyloid protein deposition. Genetic analysis identified a heterozygous de novo variant in PSEN1 (c.697A > G, p.M233V). CONCLUSIONS: Screening for PSEN1 variations should be considered in patients with levodopa-responsive early-onset parkinsonism.

Deep denoiser prior based deep analytic network for lensless image restoration.

Mask based lensless imagers have huge application prospects due to their ultra-thin body. However, the visual perception of the restored images is poor due to the ill conditioned nature of the system. In this work, we proposed a deep analytic network by imitating the traditional optimization process as an end-to-end network. Our network combines analytic updates with a deep denoiser prior to progressively improve lensless image quality over a few iterations. The convergence is proven mathematically and verified in the results. In addition, our method is universal in non-blind restoration. We detailed the solution for the general inverse problem and conducted five groups of deblurring experiments as examples. Both experimental results demonstrate that our method achieves superior performance against the existing state-of-the-art methods.

Rotated rectangular aperture imaging through multi-frame blind deconvolution with Hyper-Laplacian priors.

Rotated rectangular aperture imaging has many advantages in large aperture telephoto systems due to its lower cost and lower complexity. This technology makes it possible to build super large aperture telescopes. In this paper, we combine the ideas of deblurring with rotated rectangular aperture imaging and propose an image synthesis algorithm based on multi-frame deconvolution. In the specific reconstruction process, Hyper-Laplacian priors and sparse priors are used, and an effective solution is developed. The simulation and real shooting experiments show that our algorithm has excellent performance in visual effect and objective evaluation. The synthetic images are significantly sharper than the results of the existing methods.

Image restoration for real-world under-display imaging.

Under-display imaging technique was recently proposed to enlarge the screen-to-body ratio for full-screen devices. However, existing image restoration algorithms have difficulty generalizing to real-world under-display (UD) images, especially to images containing strong light sources. To address this issue, we propose a novel method for building a synthetic dataset (CalibPSF dataset) and introduce a two-stage neural network to solve the under-display imaging degradation problem. The CalibPSF dataset is generated using the calibrated high dynamic range point spread function (PSF) of the under-display optical system and contains various simulated light sources. The two-stage network solves the color distortion and diffraction degradation in order. We evaluate the performance of our algorithm on our captured real-world test set. Comprehensive experiments demonstrate the superiority of our method in different dynamic range scenes.