Enhanced Multiscale Human Brain Imaging by Semi-supervised Digital Staining and Serial Sectioning Optical Coherence Tomography.

A major challenge in neuroscience is to visualize the structure of the human brain at different scales. Traditional histology reveals micro- and meso-scale brain features, but suffers from staining variability, tissue damage and distortion that impedes accurate 3D reconstructions. Here, we present a new 3D imaging framework that combines serial sectioning optical coherence tomography (S-OCT) with a deep-learning digital staining (DS) model. We develop a novel semi-supervised learning technique to facilitate DS model training on weakly paired images. The DS model performs translation from S-OCT to Gallyas silver staining. We demonstrate DS on various human cerebral cortex samples with consistent staining quality. Additionally, we show that DS enhances contrast across cortical layer boundaries. Furthermore, we showcase geometry-preserving 3D DS on cubic-centimeter tissue blocks and visualization of meso-scale vessel networks in the white matter. We believe that our technique offers the potential for high-throughput, multiscale imaging of brain tissues and may facilitate studies of brain structures.

The Impact of Environmental Regulation on Hebei's Manufacturing Industry in the Global Value Chain.

In order to tackle increasingly serious environmental problems, China has been promoting the development of a green economy and guiding the green transformation of various regions and industries through environmental regulation in recent years. By participating in international trade, Hebei Province has been embedded in the global value chain. However, Hebei's involvement in the high-energy-consuming and polluting manufacturing sector and its lower position in the global value chain have led to serious environmental issues. In practice, the government has promulgated environmental regulations to restrict economic activities of enterprises. What role does environmental regulation play in Hebei's manufacturing industry's participation in the global value chain? In order to explore the impact of environmental regulation on Hebei's manufacturing industry in the global value chain, this paper constructs a fixed-effect econometric model based on the panel data of the embedding level of the value chain of 12 manufacturing sectors in Hebei Province. The research results show that: first, the R & D capacity of the manufacturing industry in Hebei Province still needs to be improved. Second, environmental regulation has promoted the global value chain position of Hebei's 12 manufacturing sectors. Third, environmental regulation will show obvious heterogeneity to manufacturing industries with different capital intensities and different pollution levels. The impact of environmental regulation on the manufacturing industry with different intensities is different. Therefore, the government should formulate targeted environmental regulation to promote the position of Hebei's manufacturing industry in the global value chain, such as further improving environmental regulation and increasing the intensity of environmental regulation and increasing the investment of human capital, and cultivating innovative talents.

Mn oxides enhanced pyrene removal with both rhizosphere and non-rhizosphere microorganisms in subsurface flow constructed wetlands.

The polycyclic aromatic hydrocarbons (PAHs) are substantial wastewater pollutants emitted mostly by petroleum refineries and petrochemical industries, and their environmental fate has been of increasing concern among the public. Consequently, subsurface flow constructed wetlands (SFCWs) filled with Mn oxides (W-CW) or without Mn oxides (K-CW) were established to investigate the performance and mechanisms of pyrene (PYR) removal. The average removal rates of PYR in W-CW and K-CW were 96.00% and 92.33%, respectively. The PYR removal via other pathways (microbial degradation, photolysis, volatilisation, etc.) occupied a sizeable proportion, while the total PYR content in K-CW plant roots was significantly higher than that of W-CW. The microorganisms on the root surface and rhizosphere played an important role in PYR degradation in W-CW and K-CW and were higher in W-CW than that in K-CW in all matrix zones. The microorganisms between the 10-16 cm zone from the bottom of W-CW filled with Mn oxides (W-16) were positively correlated with PYR-degrading microorganisms, aerobic bacteria and facultative anaerobes, whereas K-16 without birnessite-coated sand was negatively correlated with these microorganisms.

Effects and mechanisms of constructed wetlands with different substrates on N2O emission in wastewater treatment.

Nitrous oxide (N2O) emissions from constructed wetlands (CWs) are accompanying problems and have attracted much attention in recent years. CWs filled with different substrates (gravel, biochar, zeolite, and pyrite) were constructed to investigate the nitrogen removal performance and N2O emissions, which named C-CWs, B-CWs, Z-CWs, and P-CWs, respectively. C-CWs showed the poorest nitrogen removal performance in all CWs. Although B-CWs exhibited the highest fluxes of N2O emissions, the percentage of N2O emissions in nitrogen removal (0.15%) was smaller than that of C-CWs (0.18%). In addition, microbiological analysis showed that compared with C-CWs, CWs filled with biochar, zeolite, and pyrite had higher abundance of nitrifying and denitrifying microorganisms and lower abundance of N2O producing bacteria. In conclusion, biochar, zeolite, and pyrite were more favorable kinds of substrate than the conventional substrates of gravel for the nitrogen removal and reduction of N2O emissions from CWs.

Comprehensive evaluation of manganese oxides and iron oxides as metal substrate materials for constructed wetlands from the perspective of water quality and greenhouse effect.

Manganese oxides and iron oxides have been widely introduced in constructed wetlands (CWs) for sewage treatment due to their extensiveness in nature and their ability to participate in various reactions, but their effects on greenhouse gas (GHG) emissions remain unclear. Here, a set of vertical subsurface-flow CWs (Control, Fe-VSSCWs, and Mn-VSSCWs) was established to comprehensively evaluate which are the better metal substrate materials for CWs, iron oxides or manganese oxides, through water quality and the global warming potential (GWP) of nitrous oxide (N2O), methane (CH4), and carbon dioxide (CO2). The results revealed that the removal efficiencies of chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) in Mn-VSSCWs were all higher than that in Fe-VSSCWs, and manganese oxides could almost completely suppress the CH4 production and reduce GWP (from 8.15 CO2-eq/m2/h to 7.17 mg CO2-eq/m2/h), however, iron oxides promoted GWP (from 8.15 CO2-eq/m2/h to 10.84 mg CO2-eq/m2/h), so manganese oxides are the better CW substrate materials to achieve effective sewage treatment while reducing the greenhouse gas effect.

Displacement-agnostic coherent imaging through scatter with an interpretable deep neural network.

Coherent imaging through scatter is a challenging task. Both model-based and data-driven approaches have been explored to solve the inverse scattering problem. In our previous work, we have shown that a deep learning approach can make high-quality and highly generalizable predictions through unseen diffusers. Here, we propose a new deep neural network model that is agnostic to a broader class of perturbations including scatterer change, displacements, and system defocus up to 10× depth of field. In addition, we develop a new analysis framework for interpreting the mechanism of our deep learning model and visualizing its generalizability based on an unsupervised dimension reduction technique. We show that our model can unmix the scattering-specific information and extract the object-specific information and achieve generalization under different scattering conditions. Our work paves the way to a robust and interpretable deep learning approach to imaging through scattering media.

Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy.

Traditional imaging cytometry uses fluorescence markers to identify specific structures but is limited in throughput by the labeling process. We develop a label-free technique that alleviates the physical staining and provides multiplexed readouts via a deep learning-augmented digital labeling method. We leverage the rich structural information and superior sensitivity in reflectance microscopy and show that digital labeling predicts accurate subcellular features after training on immunofluorescence images. We demonstrate up to three times improvement in the prediction accuracy over the state of the art. Beyond fluorescence prediction, we demonstrate that single cell-level structural phenotypes of cell cycles are correctly reproduced by the digital multiplexed images, including Golgi twins, Golgi haze during mitosis, and DNA synthesis. We further show that the multiplexed readouts enable accurate multiparametric single-cell profiling across a large cell population. Our method can markedly improve the throughput for imaging cytometry toward applications for phenotyping, pathology, and high-content screening.

A new insight on the effects of iron oxides and dissimilated metal-reducing bacteria on CH4 emissions in constructed wetland matrix systems.

Iron oxides and dissimilated metal-reducing bacteria (DMRB) have been reported to result in a reduction in methane (CH4) emissions in constructed wetlands (CWs), but their mechanisms on CH4 production and oxidation remains unclear. Here, a set of CW matrix systems (Control, Fe-CWs, and FeB-CWs) was established to analyze the CH4 emission reduction from various angles, including the valencies of iron, microbial community structure and enzyme activity. The results revealed that the addition of iron oxides promoted the electron transfer between methanogens and Geobacter to promote CH4 production, but it was interesting that iron oxides also reduced the enzymes involved in the carbon dioxide (CO2) reduction pathway and promoted the enzymes that participated in anaerobic oxidation of methane (AOM) thereby leading to the overall reduction in CH4 emissions. Moreover, DMRB could promote iron reduction thereby further reducing CH4 emissions by promoting AOM and competing with methanogens for organic substrates.

Deep spectral learning for label-free optical imaging oximetry with uncertainty quantification.

Measurement of blood oxygen saturation (sO2) by optical imaging oximetry provides invaluable insight into local tissue functions and metabolism. Despite different embodiments and modalities, all label-free optical-imaging oximetry techniques utilize the same principle of sO2-dependent spectral contrast from haemoglobin. Traditional approaches for quantifying sO2 often rely on analytical models that are fitted by the spectral measurements. These approaches in practice suffer from uncertainties due to biological variability, tissue geometry, light scattering, systemic spectral bias, and variations in the experimental conditions. Here, we propose a new data-driven approach, termed deep spectral learning (DSL), to achieve oximetry that is highly robust to experimental variations and, more importantly, able to provide uncertainty quantification for each sO2 prediction. To demonstrate the robustness and generalizability of DSL, we analyse data from two visible light optical coherence tomography (vis-OCT) setups across two separate in vivo experiments on rat retinas. Predictions made by DSL are highly adaptive to experimental variabilities as well as the depth-dependent backscattering spectra. Two neural-network-based models are tested and compared with the traditional least-squares fitting (LSF) method. The DSL-predicted sO2 shows significantly lower mean-square errors than those of the LSF. For the first time, we have demonstrated en face maps of retinal oximetry along with a pixel-wise confidence assessment. Our DSL overcomes several limitations of traditional approaches and provides a more flexible, robust, and reliable deep learning approach for in vivo non-invasive label-free optical oximetry.

Reliable deep-learning-based phase imaging with uncertainty quantification.

Emerging deep-learning (DL)-based techniques have significant potential to revolutionize biomedical imaging. However, one outstanding challenge is the lack of reliability assessment in the DL predictions, whose errors are commonly revealed only in hindsight. Here, we propose a new Bayesian convolutional neural network (BNN)-based framework that overcomes this issue by quantifying the uncertainty of DL predictions. Foremost, we show that BNN-predicted uncertainty maps provide surrogate estimates of the true error from the network model and measurement itself. The uncertainty maps characterize imperfections often unknown in real-world applications, such as noise, model error, incomplete training data, and out-of-distribution testing data. Quantifying this uncertainty provides a per-pixel estimate of the confidence level of the DL prediction as well as the quality of the model and data set. We demonstrate this framework in the application of large space-bandwidth product phase imaging using a physics-guided coded illumination scheme. From only five multiplexed illumination measurements, our BNN predicts gigapixel phase images in both static and dynamic biological samples with quantitative credibility assessment. Furthermore, we show that low-certainty regions can identify spatially and temporally rare biological phenomena. We believe our uncertainty learning framework is widely applicable to many DL-based biomedical imaging techniques for assessing the reliability of DL predictions.