Reconstruction of diploid higher-order human 3D genome interactions from noisy Pore-C data using Dip3D
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Nano-micro pore structure characteristics of carbon black and recycled carbon fiber reinforced alkali-activated materials
Microscopic scrutiny aids in alkali-activated materials’ (AAM) application in construction industry. This study delves into the pore structure and properties of one-part alkali-activated slag (AAS) mortar modified by carbon black (CB) and recycled carbon fiber (rCF). The additives enhanced flexural strength by 51.82% (12.16 MPa) with lower water absorption (10.24%). Refinement of pore size and reduction of connectivity are key factors in improving properties. The densification effect of CB and the strong interface between rCF and gel were observed. Furthermore, AAS mortars exhibited multifractal characteristics within the range of micropores and capillary pores. Despite altering fractal regions, the additives did not affect its size dependence. The backbone fractal dimension increases with the addition of CB and rCF, exhibiting strong correlations with various macro properties, thus serving as a comprehensive parameter to characterize pore shape and distribution. This study deepens understanding of AAM composites, facilitating their adoption of low-carbon building materials.
Approaching maximum resolution in structured illumination microscopy via accurate noise modeling
Biological images captured by microscopes are characterized by heterogeneous signal-to-noise ratios (SNRs) due to spatially varying photon emission across the field of view convoluted with camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction methods, commonly implemented in the Fourier domain, often do not accurately model this noise. Such methods therefore suffer from high-frequency artifacts, user-dependent choices of smoothness constraints making assumptions on biological features, and unphysical negative values in the recovered fluorescence intensity map. On the other hand, supervised algorithms rely on large datasets for training, and often require retraining for new sample structures. Consequently, achieving high contrast near the maximum theoretical resolution in an unsupervised, physically principled manner remains an open problem. Here, we propose Bayesian-SIM (B-SIM), a Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating known noise sources in the spatial domain. To accelerate the reconstruction process, we use the finite extent of the point-spread-function to devise a parallelized Monte Carlo strategy involving chunking and restitching of the inferred fluorescence intensity. We benchmark our framework on both simulated and experimental images, and demonstrate improved contrast permitting feature recovery at up to 25% shorter length scales over state-of-the-art methods at both high- and low SNR. B-SIM enables unsupervised, quantitative, physically accurate reconstruction without the need for labeled training data, democratizing high-quality SIM reconstruction and expands the capabilities of live-cell SIM to lower SNR, potentially revealing biological features in previously inaccessible regimes.
Personalized bioceramic grafts for craniomaxillofacial bone regeneration
The reconstruction of craniomaxillofacial bone defects remains clinically challenging. To date, autogenous grafts are considered the gold standard but present critical drawbacks. These shortcomings have driven recent research on craniomaxillofacial bone reconstruction to focus on synthetic grafts with distinct materials and fabrication techniques. Among the various fabrication methods, additive manufacturing (AM) has shown significant clinical potential. AM technologies build three-dimensional (3D) objects with personalized geometry customizable from a computer-aided design. These layer-by-layer 3D biomaterial structures can support bone formation by guiding cell migration/proliferation, osteogenesis, and angiogenesis. Additionally, these structures can be engineered to degrade concomitantly with the new bone tissue formation, making them ideal as synthetic grafts. This review delves into the key advances of bioceramic grafts/scaffolds obtained by 3D printing for personalized craniomaxillofacial bone reconstruction. In this regard, clinically relevant topics such as ceramic-based biomaterials, graft/scaffold characteristics (macro/micro-features), material extrusion-based 3D printing, and the step-by-step workflow to engineer personalized bioceramic grafts are discussed. Importantly, in vitro models are highlighted in conjunction with a thorough examination of the signaling pathways reported when investigating these bioceramics and their effect on cellular response/behavior. Lastly, we summarize the clinical potential and translation opportunities of personalized bioceramics for craniomaxillofacial bone regeneration.
A computational spectrometer for the visible, near, and mid-infrared enabled by a single-spinning film encoder
Computational spectrometers enable low-cost, in-situ, and rapid spectral analysis, with applications in chemistry, biology, and environmental science. Traditional filter-based spectral encoding approaches typically use filter arrays, complicating the manufacturing process and hindering device consistency. Here we propose a computational spectrometer spanning visible to mid-infrared by combining the Single-Spinning Film Encoder (SSFE) with a deep learning-based reconstruction algorithm. Optimization through particle swarm optimization (PSO) allows for low-correlation and high-complexity spectral responses under different polarizations and spinning angles. The spectrometer demonstrates single-peak resolutions of 0.5 nm, 2 nm, 10 nm, and dual-peak resolutions of 3 nm, 6 nm, 20 nm for the visible, near, and mid-infrared wavelength ranges. Experimentally, it shows an average MSE of 1.05 × 10⁻³ for narrowband spectral reconstruction in the visible wavelength range, with average center-wavelength and linewidth errors of 0.61 nm and 0.56 nm. Additionally, it achieves an overall 81.38% precision for the classification of 220 chemical compounds, showcasing its potential for compact, cost-effective spectroscopic solutions.
Cellpose3: one-click image restoration for improved cellular segmentation
Generalist methods for cellular segmentation have good out-of-the-box performance on a variety of image types; however, existing methods struggle for images that are degraded by noise, blurring or undersampling, all of which are common in microscopy. We focused the development of Cellpose3 on addressing these cases and here we demonstrate substantial out-of-the-box gains in segmentation and image quality for noisy, blurry and undersampled images. Unlike previous approaches that train models to restore pixel values, we trained Cellpose3 to output images that are well segmented by a generalist segmentation model, while maintaining perceptual similarity to the target images. Furthermore, we trained the restoration models on a large, varied collection of datasets, thus ensuring good generalization to user images. We provide these tools as ‘one-click’ buttons inside the graphical interface of Cellpose as well as in the Cellpose API.
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