Deep-Gamma: deep low-excitation fluorescence imaging global enhancement.

Lowering the excitation to reduce phototoxicity and photobleaching while numerically enhancing the fluorescence signal is a useful way to support long-term observation in fluorescence microscopy. However, invalid features, such as near-zero gradient dark backgrounds in fluorescence images, negatively affect the neural networks due to the network training locality. This problem makes it difficult for mature deep learning-based image enhancement methods to be directly extended to fluorescence imaging enhancement. To reduce the negative optimization effect, we previously designed Kindred-Nets in conjunction with a mixed fine-tuning scheme, but the mapping learned from the fine-tuning dataset may not fully apply to fluorescence images. In this work, we proposed a new, to the best of our knowledge, deep low-excitation fluorescence imaging global enhancement framework, named Deep-Gamma, that is completely different from our previously designed scheme. It contains GammaAtt, a self-attention module that calculates the attention weights from global features, thus avoiding negative optimization. Besides, in contrast to the classical self-attention module outputting multidimensional attention matrices, our proposed GammaAtt output, as multiple parameters, significantly reduces the optimization difficulty and thus supports easy convergence based on a small-scale fluorescence microscopy dataset. As proven by both simulations and experiments, Deep-Gamma can provide higher-quality fluorescence-enhanced images compared to other state-of-the-art methods. Deep-Gamma is envisioned as a future deep low-excitation fluorescence imaging enhancement modality with significant potential in medical imaging applications. This work is open source and available at https://github.com/ZhiboXiao/Deep-Gamma.

Deep SBP+: breaking through the space-bandwidth product limit based on a physical-driven cycle constraint framework.

To obtain an image with both high spatial resolution and a large field of view (FoV), we designed a deep space-bandwidth product (SBP)-expanded framework (Deep SBP+). Combining a single-captured low-spatial-resolution image with a large FoV and a few captured high-spatial-resolution images in sub-FoVs, an image with both high spatial resolution and a large FoV can be reconstructed via Deep SBP+. The physical model-driven Deep SBP+ reconstructs the convolution kernel as well as up-samples the low-spatial resolution image in a large FoV without relying on any external datasets. Compared to conventional methods relying on spatial and spectral scanning with complicated operations and systems, the proposed Deep SBP+ can reconstruct high-spatial-resolution and large-FoV images with much simpler operations and systems as well as faster speed. Since the designed Deep SBP+ breaks through the trade-off of high spatial resolution and large FoV, it is a promising tool for photography and microscopy.

REPAID: resolution-enhanced plenoptic all-in-focus imaging using deep neural networks.

Due to limited depth-of-focus, classical 2D images inevitably lose details of targets out of depth-of-focus, while all-in-focus images break through the limit by fusing multi-focus images, thus being able to focus on targets in extended depth-of-view. However, conventional methods can hardly obtain dynamic all-in-focus imaging in both high spatial and temporal resolutions. To solve this problem, we design REPAID, meaning resolution-enhanced plenoptic all-in-focus imaging using deep neural networks. In REPAID, multi-focus images are first reconstructed from a single-shot plenoptic image, then upsampled using specially designed deep neural networks suitable for real scenes without ground truth to finally generate all-in-focus image in both high temporal and spatial resolutions. Experiments on both static and dynamic scenes have proved that REPAID can obtain high-quality all-in-focus imaging when using simple setups only; therefore, it is a promising tool in applications especially intended for imaging dynamic targets in large depth-of-view.

Deep-learning-assisted spectroscopic single-molecule localization microscopy based on spectrum-to-spectrum denoising.

Spectroscopic single-molecule localization microscopy (sSMLM) simultaneously captures spatial localizations and spectral signatures, providing the ability of multiplexed and functional subcellular imaging applications. However, extracting accurate spectral information in sSMLM remains challenging due to the poor signal-to-noise ratio (SNR) of spectral images set by a limited photon budget from single-molecule fluorescence emission and inherent electronic noise during the image acquisition using digital cameras. Here, we report a novel spectrum-to-spectrum (Spec2Spec) framework, a self-supervised deep-learning network that can significantly suppress the noise and accurately recover low SNR emission spectra from a single-molecule localization event. A training strategy of Spec2Spec was designed for sSMLM data by exploiting correlated spectral information in spatially adjacent pixels, which contain independent noise. By validating the qualitative and quantitative performance of Spec2Spec on simulated and experimental sSMLM data, we demonstrated that Spec2Spec can improve the SNR and the structure similarity index measure (SSIM) of single-molecule spectra by about 6-fold and 3-fold, respectively, further facilitating 94.6% spectral classification accuracy and nearly 100% data utilization ratio in dual-color sSMLM imaging.

SegCoFusion: An Integrative Multimodal Volumetric Segmentation Cooperating With Fusion Pipeline to Enhance Lesion Awareness.

Multimodal volumetric segmentation and fusion are two valuable techniques for surgical treatment planning, image-guided interventions, tumor growth detection, radiotherapy map generation, etc. In recent years, deep learning has demonstrated its excellent capability in both of the above tasks, while these methods inevitably face bottlenecks. On the one hand, recent segmentation studies, especially the U-Net-style series, have reached the performance ceiling in segmentation tasks. On the other hand, it is almost impossible to capture the ground truth of the fusion in multimodal imaging, due to differences in physical principles among imaging modalities. Hence, most of the existing studies in the field of multimodal medical image fusion, which fuse only two modalities at a time with hand-crafted proportions, are subjective and task-specific. To address the above concerns, this work proposes an integration of multimodal segmentation and fusion, namely SegCoFusion, which consists of a novel feature frequency dividing network named FDNet and a segmentation part using a dual-single path feature supplementing strategy to optimize the segmentation inputs and suture with the fusion part. Furthermore, focusing on multimodal brain tumor volumetric fusion and segmentation, the qualitative and quantitative results demonstrate that SegCoFusion can break the ceiling both of segmentation and fusion methods. Moreover, the effectiveness of the proposed framework is also revealed by comparing it with state-of-the-art fusion methods on 2D two-modality fusion tasks, our method achieves better fusion performance than others. Therefore, the proposed SegCoFusion develops a novel perspective that improves the performance in volumetric fusion by cooperating with segmentation and enhances lesion awareness.

RSM-Modeling and Optimization of High Titer Functional Xylo-oligosaccharides Production by Edible Gluconic Acid Catalysis.

Xylo-oligosaccharides have great value in food, feed fields. Previous studies have shown that organic acids catalyze the hydrolysis of xylan-rich sources for the production of xylo-oligosaccharides. In this study, gluconic acid of aldonic acid generated xylo-oligosaccharides via hydrolysis of xylan from corncob. In order to maximize efficiency of xylo-oligosaccharides production, the optimum conditions was ascertained by Box-Behnken design-based response surface methodology. The developed process resulted in a maximum xylo-oligosaccharides yield of 57.73% using 4.6% gluconic acid at 167 °C for 28 min, which was similar to the predicted value and fitted models of xylo-oligosaccharides production. The results showed that the reaction temperature was crucial to xylo-oligosaccharides production, and by-product yields (xylose and furfural) could be effectively controlled by both reaction temperature and time. In addition, 44.87 g/L XOS was achieved by decreasing the solid-liquid ratio. Overall, the described process may be a preferred option for future high concentration xylo-oligosaccharides production.

Multifactorial effects of gluconic acid pretreatment of waste straws on enzymatic hydrolysis performance.

The chemical compositions of lignin, hemicellulose and cellulose are so far unascertained to various lignocellulose in respect to effect of cellulose enzymatic hydrolysis. The novel and environment-friendly gluconic acid (GA) pretreatment technology showed impressive results on the enzymatic hydrolysis of cellulose in various agricultural straws. However, only few of the main reasons or critical issues pertaining to this reaction are known. Therefore, the novel GA pretreatment was carried out to remove hemicellulose from the three representative waste straws under different conditions. Next, for the enzymatic hydrolysis of the residual cellulose fraction in the pretreated straws, some mathematical correlations have been investigated between enzyme accessibility, hemicellulose removal rate, and cellulose crystallinity index. Both linear and nonlinear models were compared using five-parameter logic curve, four-parameter logic curve, and Deming regression. Hemicellulose removal was logically ascribed to be the trigger for cellulose saccharification efficiency during GA pretreatment of these waste straws.

The role of immune cells and associated immunological factors in the immune response to spinal cord injury.

Spinal cord injury (SCI) is a devastating neurological condition prevalent worldwide. Where the pathological mechanisms underlying SCI are concerned, we can distinguish between primary injury caused by initial mechanical damage and secondary injury characterized by a series of biological responses, such as vascular dysfunction, oxidative stress, neurotransmitter toxicity, lipid peroxidation, and immune-inflammatory response. Secondary injury causes further tissue loss and dysfunction, and the immune response appears to be the key molecular mechanism affecting injured tissue regeneration and functional recovery from SCI. Immune response after SCI involves the activation of different immune cells and the production of immunity-associated chemicals. With the development of new biological technologies, such as transcriptomics, the heterogeneity of immune cells and chemicals can be classified with greater precision. In this review, we focus on the current understanding of the heterogeneity of these immune components and the roles they play in SCI, including reactive astrogliosis and glial scar formation, neutrophil migration, macrophage transformation, resident microglia activation and proliferation, and the humoral immunity mediated by T and B cells. We also summarize findings from clinical trials of immunomodulatory therapies for SCI and briefly review promising therapeutic drugs currently being researched.

Automatic whole blood cell analysis from blood smear using label-free multi-modal imaging with deep neural networks.

Whole blood cell analysis is widely used in medical applications since its results are indicators for diagnosing a series of diseases. In this work, we report automatic whole blood cell analysis from blood smear using label-free multi-modal imaging with deep neural networks. First, a commercial microscope equipped with our developed Phase Real-time Microscope Camera (PhaseRMiC) obtains both bright-field and quantitative phase images. Then, these images are automatically processed by our designed blood smear recognition networks (BSRNet) that recognize erythrocytes, leukocytes and platelets. Finally, blood cell parameters such as counts, shapes and volumes can be extracted according to both quantitative phase images and automatic recognition results. The proposed whole blood cell analysis technique provides high-quality blood cell images and supports accurate blood cell recognition and analysis. Moreover, this approach requires rather simple and cost-effective setups as well as easy and rapid sample preparations. Therefore, this proposed method has great potential application in blood testing aiming at disease diagnostics.

Comprehensive investigation of multiples factors in sulfuric acid pretreatment on the enzymatic hydrolysis of waste straw cellulose.

The prerequisite for cellulosic biochemical production from lignocellulosic materials is efficient enzymatic hydrolysis that is a complicated heterogeneous catalytic process and affected by the complex lignin-cellulose-hemicellulose network. Understanding the main influencing factors for enzymatic hydrolysis is of substantial significance to guide the design of a biorefinery process. An experimental study of the pretreatment indicated that acid pretreatment is preferable for herbaceous feedstocks. Therefore, the classic dilute sulfuric acid pretreatment was utilized to hydrolyze and remove hemicellulose from three representative types of agricultural straws at various intensities. From the enzymatic hydrolysis of residual cellulose perspective, the crystallinity index and enzyme accessibility of the pretreated materials were also mathematically correlated to hemicellulose removals, respectively. For the better insight and understanding of the mathematical logics, the linear and nonlinear kinetic models were therefore compared, and the relationship was established by the five-parameter logistic equations and Allosteric sigmoidal models with well fittings.

WNT6 is an effective marker for osteosarcoma diagnosis and prognosis.

Wingless-Type MMTV Integration Site Family, Member 6 (WNT6) is a member of the Wnt family and its expression is abnormal in different human cancer cell lines. The purpose of this study was to investigate the clinical significance of WNT6 in osteosarcoma.The levels of WNT6 mRNA and protein in tissue and serum were detected through quantitative real-time polymorperase chain reaction (qRT-PCR) and Enzyme Lined Immunosorbent Assay (ELISA), respectively. Chi-square test was performed to estimate the association of WNT6 expression with clinical parameters among osteosarcoma patients. Receiver operation characteristic (ROC) curve was plotted to determine diagnostic performance of serum WNT6 in osteosarcoma. Survival analysis was performed using Kaplan-Meier method. Cox regression analysis was adopted to evaluate prognostic significance of WNT6 expression among osteosarcoma patients.Compared with the controls, WNT6 mRNA and protein levels were significantly elevated in patients with osteosarcoma (P > .05 for all). Furthermore, WNT6 upregulation showed positive correlation with patients' age (P < .001), tumor grade (P < .001) and distant metastasis (P = .001). WNT6 might be a diagnostic marker for osteosarcoma with an AUC of 0.854 combining a specificity of 88.4% and a sensitivity of 77.8%. Survival analysis result indicated that high WNT6 expression predicted poor survival (log rank test, P = .001). WNT6 might be a potential prognostic biomarker for osteosarcoma (HR = 2.227, 95%CI = 1.061-10.842, P = .027).WNT6 may be a diagnostic and prognostic marker in osteosarcoma.