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Airbnb is a platform company that provides and directs connections between hosts and guests. People who have an open room or a vacant space can become a host on Airbnb and make it available to the world community. Airbnb offers hosts an easy way to turn otherwise wasted space into profitable space. Therefore, it is particularly necessary for hosts to forecast and analyze the price of the houses they own. Machine learning is the science of developing algorithms and statistical models. The regression model is a predictive modeling technique in machine learning. This technique is often used to discover causal relationships between variables, predictive analysis, and time series models. In this project, our goal is to predict Boston Airbnb listing prices through a variety of machine-learning methods. This paper chose four regression models, which are the random forest regression model, linear regression model, K-nearest neighbor regression model, and Gradient Boosting regression model. With one of the best regression models, this paper obtained R-squared values of 0.6593 in training and 0.7198 in testing on the Boston dataset.

A fundamental challenge in fluorescence microscopy is the photon shot noise arising from the inevitable stochasticity of photon detection. Noise increases measurement uncertainty and limits imaging resolution, speed and sensitivity. To achieve high-sensitivity fluorescence imaging beyond the shot-noise limit, we present DeepCAD-RT, a self-supervised deep learning method for real-time noise suppression. Based on our previous framework DeepCAD, we reduced the number of network parameters by 94%, memory consumption by 27-fold and processing time by a factor of 20, allowing real-time processing on a two-photon microscope. A high imaging signal-to-noise ratio can be acquired with tenfold fewer photons than in standard imaging approaches. We demonstrate the utility of DeepCAD-RT in a series of photon-limited experiments, including in vivo calcium imaging of mice, zebrafish larva and fruit flies, recording of three-dimensional (3D) migration of neutrophils after acute brain injury and imaging of 3D dynamics of cortical ATP release. DeepCAD-RT will facilitate the morphological and functional interrogation of biological dynamics with a minimal photon budget.

Undercarriage device is one of the essential parts of an aeroplane, and accurate detection of whether the aeroplane undercarriage is operating normally can effectively avoid aeroplane accidents. To address the problems of low automation and low accuracy of small target detection in existing aeroplane undercarriage detection methods, an improved algorithm for aeroplane undercarriage detection YOLO V4 is proposed. Firstly, the convolutional network structure of Inception-ResNet is integrated into the CSPDarkNet53 framework to improve the algorithm’s ability to extract semantic information of target features; then an attention mechanism is added to the path aggregation network algorithm structure to improve the importance and relevance of different features after conceptual operations. In addition, aeroplane and undercarriage datasets were constructed, and finally, the generated partitioned test sets were tested to evaluate the test performance of Faster R-CNN, YOLO V3, and YOLO V4 target detection algorithms. The experimental results show that the improved algorithm has significantly improved the recall rate and the mean accuracy of detection for small targets in our dataset compared with the YOLO V4 algorithm. The reasonableness and advancedness of the improved algorithm in this paper are effectively verified.

We evaluated the potential of whey protein hydrolysate as a lyoprotectant for maintaining the cell viability of Bifidobacterium animalis ssp. lactis Probio-M8 during freeze-drying and subsequent storage. The moisture content and water activity of the lyophilized samples treated by different concentrations of whey protein hydrolysate were ≤5.23 ± 0.33 g/100 g and ≤0.102 ± 0.003, respectively. During storage at 25°C and 30°C, whey protein hydrolysate had a stronger protective effect on B. lactis Probio-M8 than the same concentration of whey protein. Using the Excel tool GinaFit, we estimated the microbial inactivation kinetics during storage. Whey protein hydrolysate reduced cell damage caused by an increase in temperature. Whey protein hydrolysate could protect cells by increasing the osmotic pressure as a compatible solute. Whey protein hydrolysate improved cell membrane integrity and reduced the amounts of reactive oxygen species and malondialdehyde produced. The findings indicated that whey protein hydrolysate was a novel antioxidant lyoprotectant that could protect probiotics during freeze-drying and storage.

As an essential step in the pathological diagnosis, histochemical staining can show specific tissue structure information and, consequently, assist pathologists in making accurate diagnoses. Clinical kidney histopathological analyses usually employ more than one type of staining: H&E, MAS, PAS, PASM, etc. However, due to the interference of colors among multiple stains, it is not easy to perform multiple staining simultaneously on one biological tissue. To address this problem, we propose a network based on unpaired training data to virtually generate multiple types of staining from one staining. Our method can preserve the content of input images while transferring them to multiple target styles accurately. To efficiently control the direction of stain transfer, we propose a style guided normalization (SGN). Furthermore, a multiple style encoding (MSE) is devised to represent the relationship among different staining styles dynamically. An improved one-hot label is also proposed to enhance the generalization ability and extendibility of our method. Vast experiments have demonstrated that our model can achieve superior performance on a tiny dataset. The results exhibit not only good performance but also great visualization and interpretability. Especially, our method also achieves satisfactory results over cross-tissue, cross-staining as well as cross-task. We believe that our method will significantly influence clinical stain transfer and reduce the workload greatly for pathologists. Our code and Supplementary materials are available at https://github.com/linyiyang98/UMDST.

High-quality magnetic resonance (MR) images afford more detailed information for reliable diagnoses and quantitative image analyses. Given low-resolution (LR) images, the deep convolutional neural network (CNN) has shown its promising ability for image super-resolution (SR). The LR MR images usually share some visual characteristics: structural textures of different sizes, edges with high correlation, and less informative background. However, multi-scale structural features are informative for image reconstruction, while the background is more smooth. Most previous CNN-based SR methods use a single receptive field and equally treat the spatial pixels (including the background). It neglects to sense the entire space and get diversified features from the input, which is critical for high-quality MR image SR. We propose a wide weighted attention multi-scale network (W2AMSN) for accurate MR image SR to address these problems. On the one hand, the features of varying sizes can be extracted by the wide multi-scale branches. On the other hand, we design a non-reduction attention mechanism to recalibrate feature responses adaptively. Such attention preserves continuous cross-channel interaction and focuses on more informative regions. Meanwhile, the learnable weighted factors fuse extracted features selectively. The encapsulated wide weighted attention multi-scale block (W2AMSB) is integrated through a recurrent framework and global attention mechanism. Extensive experiments and diversified ablation studies show the effectiveness of our proposed W2AMSN, which surpasses state-of-the-art methods on most popular MR image SR benchmarks quantitatively and qualitatively. And our method still offers superior accuracy and adaptability on real MR images.

Deep learning based algorithms have been widely employed to build reliable steel surface defect detection systems, which are important for manufacturing. The performance of deep learning models relies heavily on abundant annotated data. Nevertheless, the labeled image volume in industrial datasets is often limited. The scarcity of training data would lead to poor detection precision. To tackle this issue, we propose the first few-shot defect detection framework. Through pre-training models using data relevant to the target task, the proposed framework can produce well-trained networks with a few labeled images. Meanwhile, we release the first publicly available few-shot defect detection dataset, namely few-shot NEU-DET (FS-ND). This dataset will serve as a fair benchmark for contrasting various methods. Afterwards, we analyze the characteristics of steel surface defect detection. It is observed that the limited amount of training data can hardly cover the data distributions in practical applications. Given this observation, we develop two domain generalization strategies that enhance the appearance and scale diversity of extracted features. Furthermore, it is found that noise existing in industrial images could result in the collapse of models. To address this problem, we devise a noise regularization strategy that improves the robustness of trained models significantly. We have conducted extensive experiments to evaluate the effectiveness of our framework. The results indicate that our framework outperforms the contrasted baseline by around 15 mAP and achieves comparable performance with models trained using abundant data.

Kresling pattern origami-inspired structural design has been widely investigated using its bistable property and the single coupling degree of freedom (DOF). In order to obtain new properties or new origami-inspired structures, it needs to innovate the crease lines in the flat sheet of Kresling pattern origami. Here, we present a derivative of Kresling pattern origami—multi-triangles cylindrical origami (MTCO) with tristable property. The truss model is modified based on the switchable active crease lines during the folding motion of the MTCO. Using the energy landscape obtained from the modified truss model, the tristable property is validated and extended to Kresling pattern origami. Simultaneously, the high stiffness property of the third stable state and some special stable states are discussed. In addition, MTCO-inspired metamaterials with deployable property and tunable stiffness, and MTCO-inspired robotic arms with wide movement ranges and rich motion forms are created. These works promote research on Kresling pattern origami, and the design ideas of the metamaterials and robotic arms play a positive role in improving the stiffness of deployable structures and conceiving motion robots.

Whereas this magnetic flux leakage classifier assured the safe operation of in-service pipelines, its magnetization impact might affect pipeline steel corrosion, particularly for pipelines with flaws in service. In this paper, the weight loss method, AC impedance technique, potentiodynamic polarization technique,X-ray photoelectron spectroscopy (XPS), and finite element simulation were used to analyze the impact of magnetic field (MF) on the corrosion behavior of high-strength pipeline steel with V-groove flaws in Ku’erle simulated solution. The vertical MF increased corrosion near the left end of a V-groove slot in the Y-direction while inhibited corrosion on the right. And the perpendicular and parallel MF induced corrosion externally to the V-groove slot while inhibiting corrosion within the V-groove slot. Moreover, perpendicular and parallel MF on corrosion system are mainly due to magnetic field gradient force, whereas vertical MF is mainly related to Lorentz force.

This work was aimed to elucidate the mechanism of action of Han-Shi-Yu-Fei-decoction (HSYFD) for treating patients with mild coronavirus disease 2019 (COVID-19) based on clinical symptom-guided network pharmacology. Experimentally, an ultra-high performance liquid chromatography technique coupled with quadrupole time-of-flight mass spectrometry method was used to profile the chemical components and the absorbed prototype constituents in rat serum after its oral administration, and 11 out of 108 compounds were identified. Calculatingly, the disease targets of Han-Shi-Yu-Fei symptoms of COVID-19 were constructed through the TCMIP V2.0 database. The subsequent network pharmacology and molecular docking analysis explored the molecular mechanism of the absorbed prototype constituents in the treatment of COVID-19. A total of 42 HSYFD targets oriented by COVID-19 clinical symptom were obtained, with EGFR, TP53, TNF, JAK2, NR3C1, TH, COMT, and DRD2 as the core targets. Enriched pathway analysis yielded multiple COVID-19-related signaling pathways, such as the PI3K/AKT signaling pathway and JAK-STAT pathway. Molecular docking showed that the key compounds, such as 6-gingerol, 10-gingerol, and scopoletin, had high binding activity to the core targets like COMT, JAK2, and NR3C1. Our work also verified the feasibility of clinical symptom-guided network pharmacology analysis of chemical compounds, and provided a possible agreement between the points of views of traditional Chinese medicine and western medicine on the disease.

Hyperspectral image (HSI) reconstruction aims to recover the 3D spatial-spectral signal from a 2D measurement in the coded aperture snapshot spectral imaging (CASSI) system. The HSI representations are highly similar and correlated across the spectral dimension. Modeling the inter-spectra interactions is beneficial for HSI reconstruction. However, existing CNN-based methods show limitations in capturing spectral-wise similarity and long-range dependencies. Besides, the HSI information is modulated by a coded aperture (physical mask) in CASSI. Nonetheless, current algorithms have not fully explored the guidance effect of the mask for HSI restoration. In this paper, we propose a novel framework, Mask-guided Spectral-wise Transformer (MST), for HSI reconstruction. Specifically, we present a Spectral-wise Multi-head Self-Attention (S-MSA) that treats each spectral feature as a token and calculates self-attention along the spectral dimension. In addition, we customize a Mask-guided Mechanism (MM) that directs S-MSA to pay attention to spatial regions with high-fidelity spectral representations. Extensive experiments show that our MST significantly outperforms state-of-the-art (SOTA) methods on simulation and real HSI datasets while requiring dramatically cheaper computational and memory costs. Code and pre-trained models are available at https://github.com/caiyuanhao1998/MST/, CVPR 2022; The first Transformer-based method for snapshot compressive imaging

The rapid development of deep learning provides a better solution for the end-to-end reconstruction of hyperspectral image (HSI). However, existing learning-based methods have two major defects. Firstly, networks with self-attention usually sacrifice internal resolution to balance model performance against complexity, losing fine-grained high-resolution (HR) features. Secondly, even if the optimization focusing on spatial-spectral domain learning (SDL) converges to the ideal solution, there is still a significant visual difference between the reconstructed HSI and the truth. Therefore, we propose a high-resolution dual-domain learning network (HDNet) for HSI reconstruction. On the one hand, the proposed HR spatial-spectral attention module with its efficient feature fusion provides continuous and fine pixel-level features. On the other hand, frequency domain learning (FDL) is introduced for HSI reconstruction to narrow the frequency domain discrepancy. Dynamic FDL supervision forces the model to reconstruct fine-grained frequencies and compensate for excessive smoothing and distortion caused by pixel-level losses. The HR pixel-level attention and frequency-level refinement in our HDNet mutually promote HSI perceptual quality. Extensive quantitative and qualitative evaluation experiments show that our method achieves SOTA performance on simulated and real HSI datasets. Code and models will be released at https://github.com/caiyuanhao1998/MST, Comment: CVPR 2022

Existing leading methods for spectral reconstruction (SR) focus on designing deeper or wider convolutional neural networks (CNNs) to learn the end-to-end mapping from the RGB image to its hyperspectral image (HSI). These CNN-based methods achieve impressive restoration performance while showing limitations in capturing the long-range dependencies and self-similarity prior. To cope with this problem, we propose a novel Transformer-based method, Multi-stage Spectral-wise Transformer (MST++), for efficient spectral reconstruction. In particular, we employ Spectral-wise Multi-head Self-attention (S-MSA) that is based on the HSI spatially sparse while spectrally self-similar nature to compose the basic unit, Spectral-wise Attention Block (SAB). Then SABs build up Single-stage Spectral-wise Transformer (SST) that exploits a U-shaped structure to extract multi-resolution contextual information. Finally, our MST++, cascaded by several SSTs, progressively improves the reconstruction quality from coarse to fine. Comprehensive experiments show that our MST++ significantly outperforms other state-of-the-art methods. In the NTIRE 2022 Spectral Reconstruction Challenge, our approach won the First place. Code and pre-trained models are publicly available at https://github.com/caiyuanhao1998/MST-plus-plus., Winner of NTIRE 2022 Challenge on Spectral Reconstruction from RGB; The First Transformer-based Method for Spectral Reconstruction

At present, with the accelerated development of the global biotechnology industry, novel transgenic technologies represented by gene editing are developing rapidly. A large number of gene-edited products featuring one or a few base indels have been commercialized. These have led to great challenges in the use of traditional nucleic acid detection technology and in safety regulation for genetically modified organisms (GMOs). In this study, we developed a portable clustered regularly interspaced short palindromic repeats/CRISPR-associated proteins 12a-based (CRISPR/Cas12a-based) biosensing platform named Cas12aFVD (fast visual detection) that can be coupled with recombinase polymerase amplification (RPA) for on-site detection of mutants in gene-edited rice in one tube. The detection procedure can be accomplished in 40 min with a visible result, which can be observed by the naked eye under blue light (470–490 nm). By accurate recognition of targets based on Cas12a/CRISPR RNA (crRNA), Cas12aFVD exhibits excellent performance for the detection of two- and three-base deletions, one-base substitution, and one-base insertion mutants with a limit of detection (LOD) of 12 copies/μl showing great potential for mutant detection, especially single-base mutants. The Cas12aFVD biosensing platform is independent of laboratory conditions, making it a promising and pioneering platform for the detection of gene-edited products.

Noninvasive optical imaging through dynamic scattering media has numerous important biomedical applications but still remains a challenging task. While standard diffuse imaging methods measure optical absorption or fluorescent emission, it is also well-established that the temporal correlation of scattered coherent light diffuses through tissue much like optical intensity. Few works to date, however, have aimed to experimentally measure and process such temporal correlation data to demonstrate deep-tissue video reconstruction of decorrelation dynamics. In this work, a single-photon avalanche diode array camera is utilized to simultaneously monitor the temporal dynamics of speckle fluctuations at the single-photon level from 12 different phantom tissue surface locations delivered via a customized fiber bundle array. Then a deep neural network is applied to convert the acquired single-photon measurements into video of scattering dynamics beneath rapidly decorrelating tissue phantoms. The ability to reconstruct images of transient (0.1-0.4 s) dynamic events occurring up to 8 mm beneath a decorrelating tissue phantom with millimeter-scale resolution is demonstrated, and it is highlighted how the model can flexibly extend to monitor flow speed within buried phantom vessels.

Deep neural networks (DNNs) are widely used and demonstrated their power in many applications, such as computer vision and pattern recognition. However, the training of these networks can be time consuming. Such a problem could be alleviated by using efficient optimizers. As one of the most commonly used optimizers, stochastic gradient descent-momentum (SGD-M) uses past and present gradients for parameter updates. However, in the process of network training, SGD-M may encounter some drawbacks, such as the overshoot phenomenon. This problem would slow the training convergence. To alleviate this problem and accelerate the convergence of DNN optimization, we propose a proportional-integral-derivative (PID) approach. Specifically, we investigate the intrinsic relationships between the PID-based controller and SGD-M first. We further propose a PID-based optimization algorithm to update the network parameters, where the past, current, and change of gradients are exploited. Consequently, our proposed PID-based optimization alleviates the overshoot problem suffered by SGD-M. When tested on popular DNN architectures, it also obtains up to 50% acceleration with competitive accuracy. Extensive experiments about computer vision and natural language processing demonstrate the effectiveness of our method on benchmark data sets, including CIFAR10, CIFAR100, Tiny-ImageNet, and PTB. We have released the code at https://github.com/tensorboy/PIDOptimizer .

Automatic skin lesion analysis of dermoscopy images remains a challenging topic. In this paper, we propose an end-to-end multi-task deep learning framework for automatic skin lesion analysis. The proposed framework can perform skin lesion detection, classification, and segmentation tasks simultaneously. To address the class imbalance issue in the dataset (as often observed in medical image datasets) and meanwhile to improve the segmentation performance, a loss function based on the focal loss and the jaccard distance is proposed. During the framework training, we employ a three-phase joint training strategy to ensure the efficiency of feature learning. The proposed framework outperforms state-of-the-art methods on the benchmarks ISBI 2016 challenge dataset towards melanoma classification and ISIC 2017 challenge dataset towards melanoma segmentation , especially for the segmentation task. The proposed framework should be a promising computer-aided tool for melanoma diagnosis.

Depth images play an important role and are prevalently used in many computer vision and computational imaging tasks. However, due to the limitation of active sensing technology, the captured depth images in practice usually suffer from low resolution and noise, which prevents its further applications. To remedy this problem, in this paper, we first propose an adaptive data fidelity formulation to optimally generate each depth pixel from a mixture probability distribution, characterizing the similarity both in the depth map and the corresponding high-resolution guided color image. The proposed method is able to fit the distribution of the input depth signal as an optimization problem by maximizing the mixture probability. Furthermore, to promote the piecewise property that depth images exhibit, we propose a transferred graph Laplacian model as a regularization term, which is general and able to handle various depth recovery tasks such as super-resolution and denoising well. Specifically, each pixel within the recovered depth image is represented as a vertex in a graph with weights in connected edges representing the similarity between vertices. By minimizing the squared variations of the image signal, the task of depth image recovery can be converted to the problem of graph-based image filtering. Since the proposed graph Laplacian regularization model is able to fully exploit a priori information about the depth image, a much more accurate and robust estimation of the underlying depth can be obtained. Extensive experiment evaluations verify that the proposed method obtains recovered depth with higher quality in terms of both objective and subjective criteria, compared with most of the state-of-the-art methods.

Fast noninvasive probing of spatially varying decorrelating events, such as cerebral blood flow beneath the human skull, is an essential task in various scientific and clinical settings. One of the primary optical techniques used is diffuse correlation spectroscopy (DCS), whose classical implementation uses a single or few single-photon detectors, resulting in poor spatial localization accuracy and relatively low temporal resolution. Here, we propose a technique termed

Nowadays, with the rapid development of biotechnology, the CRISPR/Cas technology in particular has produced many new traits and products. Therefore, rapid and high-resolution detection methods for biotechnology products are urgently needed, which is extremely important for safety regulation. Recently, in addition to being gene editing tools, CRISPR/Cas systems have also been used in detection of various targets. CRISPR/Cas systems can be successfully used to detect nucleic acids, proteins, metal ions and others in combination with a variety of technologies, with great application prospects in the future. However, there are still some challenges need to be addressed. In this review, we will list some detection methods of genetically modified (GM) crops, gene-edited crops and single-nucleotide polymorphisms (SNPs) based on CRISPR/Cas systems, hoping to bring some inspiration or ideas to readers.

It is widely acknowledged that biological intelligence is capable of learning continually without forgetting previously learned skills. Unfortunately, it has been widely observed that many artificial intelligence techniques, especially (deep) neural network (NN)-based ones, suffer from catastrophic forgetting problem, which severely forgets previous tasks when learning a new one. How to train NNs without catastrophic forgetting, which is termed continual learning, is emerging as a frontier topic and attracting considerable research interest. Inspired by memory replay and synaptic consolidation mechanism in brain, in this article, we propose a novel and simple framework termed memory recall (MeRec) for continual learning with deep NNs. In particular, we first analyze the feature stability across tasks in NN and show that NN can yield task stable features in certain layers. Then, based on this observation, we use a memory module to keep the feature statistics (mean and std) for each learned task. Based on the memory and statistics, we show that a simple replay strategy with Gaussian distribution-based feature regeneration can recall and recover the knowledge from previous tasks. Together with the weight regularization, MeRec preserves weights learned from previous tasks. Based on this simple framework, MeRec achieved leading performance with extremely small memory budget (only two feature vectors for each class) for continual learning on CIFAR-10 and CIFAR-100 datasets, with at least 50% accuracy drop reduction after several tasks compared to previous state-of-the-art approaches.

The feedback mechanism in the human visual system extracts high-level semantics from noisy scenes. It then guides low-level noise removal, which has not been fully explored in image denoising networks based on deep learning. The commonly used fully-supervised network optimizes parameters through paired training data. However, unpaired images without noise-free labels are ubiquitous in the real world. Therefore, we proposed a multi-scale selective feedback network (MSFN) with the dual loss. We allow shallow layers to access valuable contextual information from the following deep layers selectively between two adjacent time steps. Iterative refinement mechanism can remove complex noise from coarse to fine. The dual regression is designed to reconstruct noisy images to establish closed-loop supervision that is training-friendly for unpaired data. We use the dual loss to optimize the primary clean-to-noisy task and the dual noisy-to-clean task simultaneously. Extensive experiments prove that our method achieves state-of-the-art results and shows better adaptability on real-world images than the existing methods.

For magnetic resonance (MR) images sharing visual characteristics, the internal structure repetitions of different scales are considerable image-specific priors. Following the traditional algorithms, we try to combine external dataset-driven learning with the internal self-similarity for MR image super-resolution (SR). We propose a pyramid orthogonal attention network (POAN) based on dual self-similarity. On the one hand, by combining the point-similarity and the pyramid-similarity, sufficient spatial autocorrelation is explored to alleviate less training data limitation. On the other hand, the non-reduction channel attention mechanism maximizes inter-channel dependence. It increases the probability of the high-frequency region (e.g., structural textures and edges) being activated while suppresses low-frequency regions (e.g., background) adaptively. Out proposed POAN reconstructs the MR image under the guidance of pyramid orthogonal attention. Extensive experiments demonstrate that our method obtains the best results compared with state-of-the-art MR image SR methods quantitatively and visually.

3D Fourier ptychographic microscopy can recover objects with high resolution across large volumes, which is very challenging in 3D imaging particularly because multiple scattering may occur within the sample. In this paper, we assume the thick sample is composed of a number of thin slices, and employ the beam propagation method (BPM) to model the propagation process of the light wave among successive slices capturing multiple scattering effects. 3D imaging is then accomplished by utilizing the gradient descent method to minimize the difference between the estimated intensity images by BPM and the captured measurements with angle-varied illuminations. We adopt a time-reversal scheme to obtain the gradient of the transmitted light intensity with respect to the complex refractive index of the volumetric sample and use the error backpropagation method to update the 3D sample iteratively. To further preserve the sharpness of the edges, we introduce a sparsity regularization term into the optimization process. Our method can achieve higher performance of 3D reconstruction compared to the original multi-slice approach, demonstrated in both simulation and experiment.

Biological activated carbon (BAC) filtration is usually considered to be able to decrease formation potentials (FPs) of disinfection by-products (DBPs) in drinking water treatment plant (DWTP). However, BAC filters with long running time may release microbial metabolites to effluents and therefore increase FPs of nitrogenous DBPs with high toxicity. To verify this hypothesis, this study continuously tracked BAC filters in a DWTP for one year, and assessed effects of old (running time 8–9 years) and new (running time 0–13 months) BAC filters on FPs of 15 regulated and unregulated DBPs. Results revealed that dissolved organic carbon (DOC) removal was slightly higher in the new BAC than the old one. All fluorescent components of dissolved organic matter evidently declined after new BAC filtration, but fulvic acid-like and soluble microbial product-like substances increased after old BAC filtration, which could be caused by microbial leakage. Correspondingly, new BAC filter generally removed more DBP FPs than the old one. 46.5% HAA7 FPs from chlorination and 44.3% THM4 FPs from chloramination were removed by new BAC filter. However, some DBP FPs, especially HAN FPs, were poorly removed or even increased by the old BAC filter. Proteobacteria could be a main contributor for DBP precursor removal in BAC filters. Herminiimonas, most abundant genera in new BAC filter, may explain its better DOC and UV254 removal performance and lower DBP FPs, while Bradyrhizobium, most abundant genera in old BAC filter, might produce more extracellular polymeric substances and therefore increased N-DBP FPs in old BAC effluent. This study provided insight into variations of DBP FPs and microbial communities in the new and old BAC filters, and will be helpful for the optimization of DWTP design and operation for public health.

The extraction of auto-correlation in images has shown great potential in deep learning networks, such as the self-attention mechanism in the channel domain and the self-similarity mechanism in the spatial domain. However, the realization of the above mechanisms mostly requires complicated module stacking and a large number of convolution calculations, which inevitably increases model complexity and memory cost. Therefore, we propose a pseudo 3D auto-correlation network (P3AN) to explore a more efficient way of capturing contextual information in image de-noising. On the one hand, P3AN uses fast 1D convolution instead of dense connections to realize criss-cross interaction, which requires less computational resources. On the other hand, the operation does not change the feature size and makes it easy to expand. It means that only a simple adaptive fusion is needed to obtain contextual information that includes both the channel domain and the spatial domain. Our method built a pseudo 3D auto-correlation attention block through 1D convolutions and a lightweight 2D structure for more discriminative features. Extensive experiments have been conducted on three synthetic and four real noisy datasets. According to quantitative metrics and visual quality evaluation, the P3AN shows great superiority and surpasses state-of-the-art image denoising methods.

Automatic liver and tumor segmentation remain a challenging topic, which subjects to the exploration of 2D and 3D contexts in CT volume. Existing methods are either only focus on the 2D context by treating the CT volume as many independent image slices (but ignore the useful temporal information between adjacent slices), or just explore the 3D context lied in many little voxels (but damage the spatial detail in each slice). These factors lead an inadequate context exploration together for automatic liver and tumor segmentation. In this paper, we propose a novel full-context convolution neural network to bridge the gap between 2D and 3D contexts. The proposed network can utilize the temporal information along the Z axis in CT volume while retaining the spatial detail in each slice. Specifically, a 2D spatial network for intra-slice features extraction and a 3D temporal network for inter-slice features extraction are proposed separately and then are guided by the squeeze-and-excitation layer that allows the flow of 2D context and 3D temporal information. To address the severe class imbalance issue in the CT volume and meanwhile improve the segmentation performance, a loss function consisting of weighted cross-entropy and jaccard distance is proposed. During the network training, the 2D and 3D contexts are learned jointly in an end-to-end way. The proposed network achieves competitive results on the Liver Tumor Segmentation Challenge (LiTS) and the 3D-IRCADB datasets. This method should be a new promising paradigm to explore the contexts for liver and tumor segmentation.

The single-image super-resolution (SISR) network based on deep learning is dedicated to learning the mapping between low-resolution (LR) images and high-resolution (HR) images. The optimal parameters of these networks often require extensive training on large-scale external image databases. For medical magnetic resonance (MR) images, there is a lack of large data sets containing high-quality images. Some deep networks that perform well on natural images cannot be fully trained on MR images, which limits the super-resolution (SR) performance. In traditional methods, the non-local self-similarity has been verified as useful statistical prior information for image restoration. The inherent feature correlation not only exists between pixels, but some patches also tend to be repeated at different positions within and across scales of MR images. Therefore, in this paper, we propose a mixed self-similarity attention network (MSAN) to explore the long-range dependencies of different regions fully. In the feature map of the entire input MR image, the prior information of self-similarity is divided into two scales: point-similarity and patch-similarity. We use points and patches that are highly similar to the current area to restore a more detailed structural texture. The internal correlation items can be used as an essential supplement to the limited external training dataset. Besides, the large number of less informative background in MR images will interfere with practical self-similarity information. A dual attention mechanism combining first-order attention and second-order attention gives more weight to salient features and suppresses the activation of useless features. Comprehensive experiments demonstrate that the proposed achieves significantly superior results on MR images SR while outperforming state-of-the-art methods by a large margin quantitatively and visually.

Effectively imaging within volumetric scattering media is of great importance and challenging especially in macroscopic applications. Recent works have demonstrated the ability to image through scattering media or within the weak volumetric scattering media using spatial distribution or temporal characteristics of the scattered field. Here, we focus on imaging Lambertian objects embedded in highly scattering media, where signal photons are dramatically attenuated during propagation and highly coupled with background photons. We address these challenges by providing a time-to-space boundary migration model (BMM) of the scattered field to convert the scattered measurements in spectral form to the scene information in the temporal domain using all of the optical signals. The experiments are conducted under two typical scattering scenarios: 2D and 3D Lambertian objects embedded in the polyethylene foam and the fog, which demonstrate the effectiveness of the proposed algorithm. It outperforms related works including time gating in terms of reconstruction precision and scattering strength. Even though the proportion of signal photons is only 0.75%, Lambertian objects located at more than 25 transport mean free paths (TMFPs), corresponding to the round-trip scattering length of more than 50 TMFPs, can be reconstructed. Also, the proposed method provides low reconstruction complexity and millisecond-scale runtime, which significantly benefits its application.

Lipid membranes are found in most intracellular organelles, and their heterogeneities play an essential role in regulating the organelles’ biochemical functionalities. Here we report a Spectrum and Polarization Optical Tomography (SPOT) technique to study the subcellular lipidomics in live cells. Simply using one dye that universally stains the lipid membranes, SPOT can simultaneously resolve the membrane morphology, polarity, and phase from the three optical-dimensions of intensity, spectrum, and polarization, respectively. These high-throughput optical properties reveal lipid heterogeneities of ten subcellular compartments, at different developmental stages, and even within the same organelle. Furthermore, we obtain real-time monitoring of the multi-organelle interactive activities of cell division and successfully reveal their sophisticated lipid dynamics during the plasma membrane separation, tunneling nanotubules formation, and mitochondrial cristae dissociation. This work suggests research frontiers in correlating single-cell super-resolution lipidomics with multiplexed imaging of organelle interactome., Lipid membranes are heterogeneous and dynamically regulated in cells. Here the authors report a Spectrum and Polarisation Optical Tomography (SPOT) method where they use Nile Red dye to resolve membrane morphology, polarity and phase in cells.

Calcium imaging is inherently susceptible to detection noise especially when imaging with high frame rate or under low excitation dosage. We developed DeepCAD, a self-supervised learning method for spatiotemporal enhancement of calcium imaging without requiring any high signal-to-noise ratio (SNR) observations. Using this method, detection noise can be effectively suppressed and the imaging SNR can be improved more than tenfold, which massively improves the accuracy of neuron extraction and spike inference and facilitate the functional analysis of neural circuits.

Calcium imaging has transformed neuroscience research by providing a methodology for monitoring the activity of neural circuits with single-cell resolution. However, calcium imaging is inherently susceptible to detection noise, especially when imaging with high frame rate or under low excitation dosage. Here we developed DeepCAD, a self-supervised deep-learning method for spatiotemporal enhancement of calcium imaging data that does not require any high signal-to-noise ratio (SNR) observations. DeepCAD suppresses detection noise and improves the SNR more than tenfold, which reinforces the accuracy of neuron extraction and spike inference and facilitates the functional analysis of neural circuits. DeepCAD is a self-supervised deep-learning approach for denoising calcium imaging data. DeepCAD improved SNR and facilitates neuron extraction and spike inference.

Light-field (LF) camera holds great promise for passive/general depth estimation benefited from high angular resolution, yet suffering small baseline for distanced region. While stereo solution with large baseline is superior to handle distant scenarios, the problem of limited angular resolution becomes bothering for near objects. Aiming for all-in-depth solution, we propose a cross-baseline LF camera using a commercial LF camera and a monocular camera, which naturally form a 'stereo camera' enabling compensated baseline for LF camera. The idea is simple yet non-trivial, due to the significant angular resolution gap and baseline gap between LF and stereo cameras. Fusing two depth maps from LF and stereo modules in spatial domain is fluky, which relies on the imprecisely predicted depth to distinguish close or distance range, and determine the weights for fusion. Alternatively, taking the unified representation for both LF and monocular sub-aperture view in epipolar plane image (EPI) domain, we show that for each pixel, the minimum variance along different shearing degrees in EPI domain estimates its depth with the highest fidelity. By minimizing the minimum variance, the depth error is minimized accordingly. The insight is that the calculated minimum variance in EPI domain owns higher fidelity than the predicted depth in spatial domain. Extensive experiments demonstrate the superiority of our cross-baseline LF camera in providing high-quality all-in-depth map from 0.2m to 100m.