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Liver is dynamic, heterogeneous, and each cell type acts in concert to regulate its function. Currently, heterogeneous liver tissues are typically built from single cells using bioprinting, making crosstalk between cells difficult. Therefore, in vitro morphogenesis is limited, and self-assembled biliary and blood vessels system are absent from manufactured liver tissues. The combination of bioprinting and organoid technique offers spatial and cellular control over three-dimensional (3D) organ tissue manufacturing, allowing to build liver tissues with self-assembled structure in vitro. We developed a high-throughput PDMS microwell platform (PMP) generating uniform and functional hepatic organoid building blocks (HOBBs) which displayed cellular crosstalk and self-assembled structure. For bioprinting process, we developed three-level temperature control system and new quadratic material, i.e., alginate-gelatin-collagen-laminin (AGCL) biomaterial, realizing reproducible construction of liver tissues with requisite cellular density. Under long-term differentiation, bioprinted liver tissues exhibited enhanced hepatobiliary function, intrahepatic bile duct networks and angiogenic potential. Heterogeneous tissues with coexistence of cholangiocytes, endothelial cells, and hepatocytes was constructed. The heterogeneous liver tissues with angiogenesis and bile duct component (HABs) provides a novel tool for morphogenesis study, liver regeneration, drug testing, and disease research.

We present two distinct, complementary methods for improving axial resolution in three-dimensional structured illumination microscopy (3D SIM) with minimal or no modification to the optical system. First, we show that placing a mirror directly opposite the sample enables 4-beam interference with higher spatial frequency content than 3D SIM illumination, offering near-isotropic imaging with ∼120 nm lateral and 160 nm axial resolution. Second, we develop an improved deep learning method that can be directly applied to 3D SIM data, obviating the need for additional hardware. This procedure results in ∼120 nm isotropic resolution and can be combined with denoising to facilitate volumetric imaging spanning dozens of time points. We demonstrate the potential of these advances by imaging a variety of cellular samples, delineating the nanoscale distribution of vimentin and microtubule filaments, observing the relative positions of caveolar coat proteins and lysosomal markers, and visualizing rich cytoskeletal dynamics within T-cells in the early stages of immune synapse formation.

The intestine is critical for not only processing and resorbing nutrients but also protecting the organism from the environment. These functions are mainly carried out by the epithelium, which is constantly being self-renewed. Many genes and pathways can influence intestinal epithelial cell proliferation. Among them is mTORC1, whose activation increases cell proliferation. Here, we report the first intestinal epithelial cell-specific knockout (ΔIEC) of an amino acid transporter capable of activating mTORC1. We show that the transporter, SLC7A5, is highly expressed in mouse intestinal crypt andSlc7a5ΔIECreduces mTORC1 signaling. Surprisingly,Slc7a5ΔIECmice have increased cell proliferation but reduced secretory cells, particularly mature Paneth cells. scRNA-seq and electron microscopic analyses revealed dedifferentiation of Paneth cells inSlc7a5ΔIECmice, leading to markedly reduced secretory granules with little effect on Paneth cell number. We further show thatSlc7a5ΔIECmice are prone to experimental colitis. Thus, SLC7A5 regulates secretory cell differentiation to affect stem cell niche and/or inflammatory response to regulate cell proliferation.

Confocal microscopy1 remains a major workhorse in biomedical optical microscopy owing to its reliability and flexibility in imaging various samples, but suffers from substantial point spread function anisotropy, diffraction-limited resolution, depth-dependent degradation in scattering samples and volumetric bleaching2. Here we address these problems, enhancing confocal microscopy performance from the sub-micrometre to millimetre spatial scale and the millisecond to hour temporal scale, improving both lateral and axial resolution more than twofold while simultaneously reducing phototoxicity. We achieve these gains using an integrated, four-pronged approach: (1) developing compact line scanners that enable sensitive, rapid, diffraction-limited imaging over large areas; (2) combining line-scanning with multiview imaging, developing reconstruction algorithms that improve resolution isotropy and recover signal otherwise lost to scattering; (3) adapting techniques from structured illumination microscopy, achieving super-resolution imaging in densely labelled, thick samples; (4) synergizing deep learning with these advances, further improving imaging speed, resolution and duration. We demonstrate these capabilities on more than 20 distinct fixed and live samples, including protein distributions in single cells; nuclei and developing neurons in Caenorhabditis elegans embryos, larvae and adults; myoblasts in imaginal disks of Drosophila wings; and mouse renal, oesophageal, cardiac and brain tissues. A combination of multiview imaging, structured illumination, reconstruction algorithms and deep-learning predictions realizes spatial- and temporal-resolution improvements in fluorescence microscopy to produce super-resolution images from diffraction-limited input images.

Tissue clearing of whole intact organs has enhanced imaging by enabling the exploration of tissue structure at a subcellular level in three-dimensional space. Although clearing and imaging of the whole organ have been used to study tissue biology, the microenvironment in which cells evolve to adapt to biomaterial implants or allografts in the body is poorly understood. Obtaining high-resolution information from complex cell-biomaterial interactions with volumetric landscapes represents a key challenge in the fields of biomaterials and regenerative medicine. To provide a new approach to examine how tissue responds to biomaterial implants, we apply cleared tissue light-sheet microscopy and three-dimensional reconstruction to utilize the wealth of autofluorescence information for visualizing and contrasting anatomical structures. This study demonstrates the adaptability of the clearing and imaging technique to provide sub-cellular resolution (0.6μm isotropic) 3D maps of various tissue types, using samples from fully intact peritoneal organs to volumetric muscle loss injury specimens, with and without biomaterials implants. We further apply computational-driven image classification to analyze the autofluorescence spectrum at multiple emission wavelengths to categorize tissue types in the tissue-biomaterial microenvironment.

Remote sensing has been used as an important means of monitoring crop growth, especially for the monitoring of the formation of crop yield in the middle and late growth period. The information acquisition on the yield formation period of winter wheat is of great significance for winter wheat growth monitoring, yield estimation and scientific management. Hence, the main goal of this study was to verify the possibility of monitoring the grain-filling process of winter wheat and its in-field variability using an alternative non-destructive method based on orbital remote sensing. High-resolution satellite imageries (3 m) were obtained from the PlanetScope platform for three commercial winter wheat fields in Jiangsu Province, China during the reproductive stage of the winter wheat (185–215/193–223/194–224 days after sowing (DAS)). Based on the quantitative analysis of vegetation indices (VIs) obtained from high-resolution satellite imageries and three indicators of the winter wheat grain-filling process, linear, polynomial and logistic growth models were used to establish the relationship between VIs and the three indicators. The research showed a high Pearson correlation (p < 0.001) between winter wheat maturity and most VIs. In the overall model, the remote sensing inversion of the dry thousand-grain weight has the highest accuracy and its R2 reaches more than 0.8, which is followed by fresh thousand-grain weight and water content, the accuracies of which are also considerable. The results indicated a great potential to use high-resolution satellite imageries to monitor winter wheat maturity variability in fields and subfields. In addition, the proposed method contributes to monitoring the dynamic spatio-temporality of the grain-filling progression, allowing for more accurate management strategies in regard to winter wheat.

Objective: Inbreeding depression of reproduction is a major concern in the conservation of native chicken genetic resources. Here, based on the successful development of strongly inbred (Sinb) and weakly inbred (Winb) Langshan chickens, we aimed to evaluate inbreeding effects on reproductive traits and identify candidate genes involved in inbreeding depression of reproduction in Langshan chickens.Methods: A two-sample t-test was performed to estimate the differences in phenotypic values of reproductive traits between Sinb and Winb chicken groups. Three healthy chickens with reproductive trait values around the group mean values were selected from each of the groups. Differences in ovarian and hypothalamus transcriptomes between the two groups of chickens were analyzed by RNA sequencing (RNA-Seq).Results: The Sinb chicken group showed an obvious inbreeding depression in reproduction, especially for traits of age at the first egg and egg number at 300 days (p

We present Richardson–Lucy network (RLN), a fast and lightweight deep learning method for three-dimensional fluorescence microscopy deconvolution. RLN combines the traditional Richardson–Lucy iteration with a fully convolutional network structure, establishing a connection to the image formation process and thereby improving network performance. Containing only roughly 16,000 parameters, RLN enables four- to 50-fold faster processing than purely data-driven networks with many more parameters. By visual and quantitative analysis, we show that RLN provides better deconvolution, better generalizability and fewer artifacts than other networks, especially along the axial dimension. RLN outperforms classic Richardson–Lucy deconvolution on volumes contaminated with severe out of focus fluorescence or noise and provides four- to sixfold faster reconstructions of large, cleared-tissue datasets than classic multi-view pipelines. We demonstrate RLN’s performance on cells, tissues and embryos imaged with widefield-, light-sheet-, confocal- and super-resolution microscopy.

Space weather phenomena threaten the space assets that bring us services via space technologies, such as the Global Positioning System, communication systems with satellite relays, and most global TV broadcast networks, which have provided unprecedented convenience to everyday life and opportunities to businesses. A hazard among phenomena1 is the population of relativistic electrons in the region called Van Allan radiation belts2. These electrons can be trapped for years once produced by either natural3 or artificial processes4 and can damage the electronics and degrade the solar panels on satellites. Intense investigations have begun with recently launched NASA satellites, Van Allen Belt Probes A and B in 20125-13. To remedy the threat and reduce the resulting damage, artificial processes can be introduced to shorten the lifetime of these particles14 with mechanisms such as pitch-angle diffusion through wave-particle interaction15-17 by transmitting very-low-frequency (VLF) waves into radiation belts. To directly transmit the VLF waves in space is an extremely challenging task, and previous theoretical and numerical predictions of the radiation impedance differ more than five orders in magnitude18-23. Here we show the measurements of radiation impedance from high-power VLF wave transmission experiments in the radiation belts to help settle the dispute of the previous studies. The measured radiation reactance disagrees with the most influential theoretical model18,19,22 and the vacuum model, but proves the plasma sheath model and simulation of the antenna-plasma interaction20,21,23. A new discovery is that the measured radiation resistance decreases as the transmission frequency increases. Our results demonstrate the possibility to transmit high power in space and validated the design and technology for further high-power space-borne VLF transmitters. The physical understanding obtained in this study will also provide a guide to laboratory whistler mode wave injection experiments24, especially in controlled fusion25.

We present a deep learning method which incorporates the Richardson-Lucy formula into a fully convolutional network and demonstrate its performance on cells, tissues, and embryos imaged with widefield-, confocal- light sheet-, and super-resolution microscopy.

Asymmetric signaling and organization in the stem-cell niche determine stem-cell fates. Here, we investigate the basis of asymmetric signaling and stem-cell organization using the Drosophila wing-disc that creates an adult muscle progenitor (AMP) niche. We show that AMPs extend polarized cytonemes to contact the disc epithelial junctions and adhere themselves to the disc/niche. Niche-adhering cytonemes localize FGF-receptor to selectively adhere to the FGF-producing disc and receive FGFs in a contact-dependent manner. Activation of FGF signaling in AMPs, in turn, reinforces disc-specific cytoneme polarity/adhesion, which maintains their disc-proximal positions. Loss of cytoneme-mediated adhesion promotes AMPs to lose niche occupancy and FGF signaling, occupy a disc-distal position, and acquire morphological hallmarks of differentiation. Discrete AMP organization and diversification patterns are determined by localized expression and presentation patterns of two different FGFs in the wing-disc and their polarized target-specific distribution through niche-adhering cytonemes. Thus, cytonemes are essential for asymmetric signaling and niche-specific AMP organization.

Asymmetric signaling and organization in the stem-cell niche determine stem-cell fates. Here, we investigate the basis of asymmetric signaling and stem-cell organization using the Drosophila wing-disc that creates an adult muscle progenitor (AMP) niche. We show that AMPs extend polarized cytonemes to contact the disc epithelial junctions and adhere themselves to the disc/niche. Niche-adhering cytonemes localize FGF-receptor to selectively adhere to the FGF-producing disc and receive FGFs in a contact-dependent manner. Activation of FGF signaling in AMPs, in turn, reinforces disc-specific cytoneme polarity/adhesion, which maintains their disc-proximal positions. Loss of cytoneme-mediated adhesion promotes AMPs to lose niche occupancy and FGF signaling, occupy a disc-distal position, and acquire morphological hallmarks of differentiation. Niche-specific AMP organization and diversification patterns are determined by localized expression and presentation patterns of two different FGFs in the wing-disc and their polarized target-specific distribution through niche-adhering cytonemes. Thus, cytonemes are essential for asymmetric signaling and niche-specific AMP organization.

Background Adult stem cells play an essential role in adult organ physiology and tissue repair and regeneration. While much has been learnt about the property and function of various adult stem cells, the mechanisms of their development remain poorly understood in mammals. Earlier studies suggest that the formation of adult mouse intestinal stem cells takes place during the first few weeks after birth, the postembryonic period when plasma thyroid hormone (T3) levels are high. Furthermore, deficiency in T3 signaling leads to defects in adult mouse intestine, including reduced cell proliferation in the intestinal crypts, where stem cells reside. Our earlier studies have shown that protein arginine methyltransferase 1 (PRMT1), a T3 receptor coactivator, is highly expressed during intestinal maturation in mouse. Methods We have analyzed the expression of PRMT1 by immunohistochemistry and studied the effect of tissue-specific knockout of PRMT1 in the intestinal epithelium. Results We show that PRMT1 is expressed highly in the proliferating transit amplifying cells and crypt base stem cells. By using a conditional knockout mouse line, we have demonstrated that the expression of PRMT1 in the intestinal epithelium is critical for the development of the adult mouse intestine. Specific removal of PRMT1 in the intestinal epithelium results in, surprisingly, more elongated adult intestinal crypts with increased cell proliferation. In addition, epithelial cell migration along the crypt-villus axis and cell death on the villus are also increased. Furthermore, there are increased Goblet cells and reduced Paneth cells in the crypt while the number of crypt base stem cells remains unchanged. Conclusions Our finding that PRMT1 knockout increases cell proliferation is surprising considering the role of PRMT1 in T3-signaling and the importance of T3 for intestinal development, and suggests that PRMT1 likely regulates pathways in addition to T3-signaling to affect intestinal development and/or homeostasis, thus affecting cell proliferating and epithelial turn over in the adult.

Confocal microscopy

SummaryWe enhance the performance of confocal microscopy over imaging scales spanning tens of nanometers to millimeters in space and milliseconds to hours in time, improving volumetric resolution more than 10-fold while simultaneously reducing phototoxicity. We achieve these gains via an integrated, four-pronged approach: 1) developing compact line-scanners that enable sensitive, rapid, diffraction-limited imaging over large areas; 2) combining line-scanning with multiview imaging, developing reconstruction algorithms that improve resolution isotropy and recover signal otherwise lost to scattering; 3) adapting techniques from structured illumination microscopy, achieving super-resolution imaging in densely labeled, thick samples; 4) synergizing deep learning with these advances, further improving imaging speed, resolution and duration. We demonstrate these capabilities on more than twenty distinct fixed and live samples, including protein distributions in single cells; nuclei and developing neurons inCaenorhabditis elegansembryos, larvae, and adults; myoblasts inDrosophilawing imaginal disks; and mouse renal, esophageal, cardiac, and brain tissues.

A belief in communism refers to the unquestionable trust and belief in the justness of communism. Although former studies have discussed the political aim and social value of communism, the cognitive neural basis of a belief in communism remains largely unknown. In this study, we determined the behavioral and neural correlates between a belief in communism and a theory of mind (ToM). For study 1, questionnaire scores were measured and for study 2, regional homogeneity (ReHo) and resting-state functional connectivity (rsFC) were used as an index for resting-state functional MRI (rs-fMRI), as measured by the Belief in Communism Scale (BCS). The results showed that a belief in communism is associated with higher ReHo in the left thalamus and lower ReHo in the left medial frontal gyrus (MFG). Furthermore, the results of the rsFC analysis revealed that strength of functional connectivity between the left thalamus and the bilateral precuneus is negatively associated with a belief in communism. Hence, this study provides evidence that spontaneous brain activity in multiple regions, which is associated with ToM capacity, contributes to a belief in communism.

As rarely large flake graphite (9 mesh) was recently exploited in China, it was innovatively developed as the raw material to prepare a novel wound dressing based on large expanded graphite (EG) in this work. The EG worms were prepared in an easy oxidative intercalation and thermal expansion method. Afterward, chitosan was grafted onto the surface of EG by chemical modification, forming CS-EG worms. CS-EG sponge dressings were then obtained by pressing a number of CS-EG worms together by external force. Due to the porous structure and large specific surface area, the produced CS-EG sponges exhibited outstanding adsorption capacity for wound exudate. They could also promote blood coagulation by adsorbing the blood cells and proteins quickly and effectively, showing excellent hemostatic performance. The eminent performances and the simple preparation process ensure the great application potential of CS-EG as a dressing material. This is also the first time to report the application of the traditional carbon material, EG, to act as a dressing material after chemical modification.

The modern fluorescence microscope would ideally offer high spatial resolution in all dimensions, high speed and minimal photodamage to specimens. We developed a triple-view line confocal system that improves spatial resolution in all three dimensions (to ~270 nm x 250 nm x 335 nm or ~185 nm x 170 nm x 245 nm with structured illumination) in scattering samples tens of microns thick. To speed up acquisition and reduce photobleaching, deep learning is applied to denoise and enhance resolution when only using data acquired from one view.

We demonstrate diffraction-limited and super-resolution imaging through thick layers (tens-hundreds of microns) of BIO-133, a biocompatible, UV-curable, commercially available polymer with a refractive index (RI) matched to water. We show that cells can be directly grown on BIO-133 substrates without the need for surface passivation and use this capability to perform extended time-lapse volumetric imaging of cellular dynamics 1) at isotropic resolution using dual-view light-sheet microscopy, and 2) at super-resolution using instant structured illumination microscopy. BIO-133 also enables immobilization of 1) Drosophila tissue, allowing us to track membrane puncta in pioneer neurons, and 2) Caenorhabditis elegans, which allows us to image and inspect fine neural structure and to track pan-neuronal calcium activity over hundreds of volumes. Finally, BIO-133 is compatible with other microfluidic materials, enabling optical and chemical perturbation of immobilized samples, as we demonstrate by performing drug and optogenetic stimulation on cells and C. elegans.

Utilizing the demographic information of social media users is very essential for personalized online services. However, it is difficult to collect such information in most realistic scenarios. Luckily, the reviews posted by users can provide rich clues for inferring their demographics, since users with different demographics such as gender and age usually have differences in their contents and expressing styles. In this paper, we propose a neural approach for demographic prediction based on user reviews. The core of our approach is a deep multi-view multi-task learning model. Our model first learns context representations from reviews using a context encoder, which takes semantics and syntactics into consideration. Meanwhile, we learn sentiment and topic representations from selected sentiment and topic words using a word encoder separately, which consists of a convolutional neural network to capture the local contexts of reviews in word-level. Then, we learn a unified user representation from context, sentiment and topic representations and apply multi-task learning for inferring user’s gender and age simultaneously. Experimental results on three real-world datasets validate the effectiveness of our approach. To facilitate future research, we release the codes and datasets at https://github.com/icmpnorequest/DASFAA2021_DMVMT.

We demonstrate diffraction-limited and super-resolution imaging through thick layers (tens-hundreds of microns) of BIO-133, a biocompatible, UV-curable, commercially available polymer with a refractive index (RI) matched to water. We show that cells can be directly grown on BIO-133 substrates without the need for surface passivation and use this capability to perform extended time-lapse volumetric imaging of cellular dynamics 1) at isotropic resolution using dual-view light-sheet microscopy, and 2) at super-resolution using instant structured illumination microscopy. BIO-133 also enables immobilization of 1)Drosophilatissue, allowing us to track membrane puncta in pioneer neurons, and 2)Caenorhabditis elegans, which allows us to image and inspect fine neural structure and to track pan-neuronal calcium activity over hundreds of volumes. Finally, BIO-133 is compatible with other microfluidic materials, enabling optical and chemical perturbation of immobilized samples, as we demonstrate by performing drug and optogenetic stimulation on cells andC. elegans.

We demonstrate residual channel attention networks (RCAN) for restoring and enhancing volumetric time-lapse (4D) fluorescence microscopy data. First, we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy 4D super-resolution data, enabling image capture over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables class-leading resolution enhancement, superior to other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling ~2.5-fold lateral resolution enhancement using stimulated emission depletion (STED) microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy ground truth, achieving improvements of ~1.4-fold laterally and ~3.4-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluating and further enhancing network performance.

We demonstrate residual channel attention networks (RCAN) for the restoration and enhancement of volumetric time-lapse (four-dimensional) fluorescence microscopy data. First we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy four-dimensional super-resolution data, enabling image capture of over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables resolution enhancement equivalent to, or better than, other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling ~2.5-fold lateral resolution enhancement using stimulated emission depletion microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy data as ground truth, achieving improvements of ~1.9-fold laterally and ~3.6-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluation and further enhancement of network performance. Three-dimensional residual channel attention networks (RCAN) enable improved image denoising and resolution enhancement on volumetric time-lapse fluorescence microscopy data, allowing longitudinal super-resolution imaging of living samples.

Point-of-interest (POI) recommendation has become an important service to help users discover attractive locations. A variety of available check-in data make it possible to build a personalized POI recommender system, but the extreme sparsity of check-in data poses a severe challenge for POI recommendation. Recent studies mainly utilize social information, categorical information and/or geographical information to supplement the highly sparse check-in data. However, these studies often apply shallow methods for the extra information and provide considerably limited improvements on POI recommendation. In this paper, we propose a fine-grained POI recommendation framework, called FGRec to capture the intrinsic influences of social, categorical and geographical information on the check-in behaviors of users. First, we study the social influence in depth by exploiting the multi-hop social friends and top-n nearest neighbor friends, not only the direct friends (i.e., 1-hop friends). Second, we investigate the categorical influence by factorizing both user-POI and user-category matrices simultaneously over the same user embedding space, rather than simply using the popularity of POI categories. Third, we explore the geographical influence by integrating two types of distance (i.e., the distance between user homes and POIs and the distance among POIs) into a unified probability distribution over check-in POIs, instead of modeling them separately. Finally, experimental results on two large-scale real-world datasets demonstrate the effectiveness and superiority of the proposed method.

User Alignment is to find users belonging to a same real person on different social networks and has become a fundamental task for many sequent applications such as cross-network recommendation systems. When matching users in multiple social networks, existing approaches always know some correctly matched users, which can be called seeds. Then, existing methods strongly depend on the neighboring users of each user to propagate alignment information from seeds and align probable matching users implicitly. However, the completeness and validity of original alignment information among seeds cannot be fully preserved when learning and aligning multiple user spaces. In this paper, we propose a unified framework named Jumping Seed Alignment Information Propagation (JSAIP) to flexibly leverage, for each user, complete and correct alignment information from seeds. Specifically, JSAIP learns a reasonable user space for each social network by preserving enough original network and label information. Then, JSAIP ensures the correct alignment among seeds and shared labels to reduce the diversity between different user spaces. Finally, JSAIP constructs jumping links from seeds to each user in each social network and ultilizes original seed alignment information to enhance or rectify the alignment information propagated from neighbors. Experiments on real world datasets demonstrate the effectiveness of our proposed JSAIP method compared to several state-of-the-art methods.

With the popularity of location-based social networks (LBSNs), Point-of-Interest (POI) recommendation has become an essential location-based service to help people explore novel locations. Although the massive check-in data bring a good opportunity, there are still many challenges in building personalized POI recommender systems based on geographical information. First, current coarse-grained geographical models provide considerably limited improvements on POI recommendations and fail to capture the overall impact of fine-grained geographical characteristics in LBSNs. Second, previous methods such as matrix factorization always give equal weight to each positive example and may not distinguish between their different contributions in learning the objective function. To cope with these challenges, we develop a fine-grained POI recommendation framework that makes full use of the geographical characteristics from both users' and locations' perspectives. For capturing the fine-grained geographical influence, we present a unified probability distribution model based on four key geographical characteristics. For mining more contribution information from positive examples, we assign a higher weight to highlight the contribution of a higher check-in frequency by employing a logistic matrix factorization. Finally, experimental results on two real-world datasets demonstrate the effectiveness and superiority of the proposed method.

There is a significant style overfitting problem in neural-based character recognition: insufficient generalization ability to recognize characters with unseen styles. To address this problem, we propose a novel framework named Style-Melt Nets (SMN), which disentangles the style and content factors to extract pure content feature. In this framework, a pair of parallel style net and content net is designed to respectively infer the style labels and content labels of input character images, and the style feature produced by the style net is fed to the content net for eliminating the style influence on content feature. In addition, the marginal distribution of character pixels is considered as an important structure indicator for enhancing the content representations. Furthermore, to increase the style diversity of training data, an efficient data augmentation approach for changing the thickness of the strokes and generating outline characters is presented. Extensive experimental results demonstrate the benefit of our methods, and the proposed SMN is able to achieve the state-ofthe-art performance on multiple real world character sets.

The contrast and resolution of images obtained with optical microscopes can be improved by deconvolution and computational fusion of multiple views of the same sample, but these methods are computationally expensive for large datasets. Here we describe theoretical and practical advances in algorithm and software design that result in image processing times that are tenfold to several thousand fold faster than with previous methods. First, we show that an ‘unmatched back projector’ accelerates deconvolution relative to the classic Richardson–Lucy algorithm by at least tenfold. Second, three-dimensional image-based registration with a graphics processing unit enhances processing speed 10- to 100-fold over CPU processing. Third, deep learning can provide further acceleration, particularly for deconvolution with spatially varying point spread functions. We illustrate our methods from the subcellular to millimeter spatial scale on diverse samples, including single cells, embryos and cleared tissue. Finally, we show performance enhancement on recently developed microscopes that have improved spatial resolution, including dual-view cleared-tissue light-sheet microscopes and reflective lattice light-sheet microscopes. Microscopy datasets are processed orders-of-magnitude faster with improved algorithms and deep learning.

Fetal hematopoietic stem and progenitor cells (HSPCs) hold promise to cure a wide array of hematological diseases, and we previously found a role for the RNA-binding protein (RBP) Lin28b in respecifying adult HSPCs to resemble their fetal counterparts. Here we show by single-cell RNA sequencing that Lin28b alone was insufficient for complete reprogramming of gene expression from the adult toward the fetal pattern. Using proteomics and in situ analyses, we found that Lin28b (and its closely related paralog, Lin28a) directly interacted with Igf2bp3, another RBP, and their enforced co-expression in adult HSPCs reactivated fetal-like B-cell development in vivo more efficiently than either factor alone. In B-cell progenitors, Lin28b and Igf2bp3 jointly stabilized thousands of mRNAs by binding at the same sites, including those of the B-cell regulators Pax5 and Arid3a as well as Igf2bp3 mRNA itself, forming an autoregulatory loop. Our results suggest that Lin28b and Igf2bp3 are at the center of a gene regulatory network that mediates the fetal–adult hematopoietic switch. A method to efficiently generate induced fetal-like hematopoietic stem cells (ifHSCs) will facilitate basic studies of their biology and possibly pave a path toward their clinical application.

The increasing prevalence of location-based social networks (LBSNs) poses a wonderful opportunity to build per-sonalized point-of-interest (POI) recommendations, which aim at recommending a top-N ranked list of POIs to users according to their preferences. Although previous studies on collaborative filtering are widely applied for POI recommendation, there are two significant challenges have not been solved perfectly. (1) These approaches cannot effectively and efficiently exploit unobserved feedback and are also unable to learn useful information from it. (2) How to seamlessly integrate multiple types of context information into these models is still under exploration. To cope with the aforementioned challenges, we develop a new Personalized pairwise Ranking Framework based on Poisson Factor factorization (PRFPF) that follows the assumption that users’ preferences for visited POIs are preferred over potential POIs, unvisited POIs are less preferred than potential POIs. The framework PRFPF is composed of two modules: candidate module and ranking module. Specifically, the candidate module is used to generate a series of potential POIs from unvisited POIs by incorporating multiple types of context information (e.g., social and geographical information). The ranking module learns the ultimate order of users’ preference by leveraging the potential POIs. Experimental results evaluated on two large-scale real-world datasets show that our framework outperforms other state-of-the-art approaches in terms of various metrics.

We describe theoretical and practical advances in algorithm and software design, resulting in ten to several thousand-fold faster deconvolution and multiview fusion than previous methods. First, we adapt methods from medical imaging, showing that an unmatched back projector accelerates Richardson-Lucy deconvolution by at least 10-fold, in most cases requiring only a single iteration. Second, we show that improvements in 3D image-based registration with GPU processing result in speedups of 10-100-fold over CPU processing. Third, we show that deep learning can provide further accelerations, particularly for deconvolution with a spatially varying point spread function. We illustrate the power of our methods from the subcellular to millimeter spatial scale, on diverse samples including single cells, nematode and zebrafish embryos, and cleared mouse tissue. Finally, we show that our methods facilitate the use of new microscopes that improve spatial resolution, including dual-view cleared tissue light-sheet microscopy and reflective lattice light-sheet microscopy.

Demographic attributes are crucial for characterizing different types of users in developing market strategy. However, in retail scenario, individual demographic information is not often available due to the difficult manual collection process. Several studies focus on inferring users’ demographic attribute based on their transaction histories, but there is a common problem. Hardly work has introduced knowledge for purchase data embedding. Specifically, purchase data is informative, full of related knowledge entities and common sense. However, existing methods are unaware of such external knowledge and latent knowledge-level connections among items. To address the above problem, we propose a Knowledge-Aware Embedding (KAE) method that incorporates knowledge graph representation into demographic prediction. The KAE is a multi-channel and item-entity-aligned knowledge-aware convolutional neural network that fuses frequency-level and knowledge-level representations of purchase data. Through extensive experiments on a real world dataset, we demonstrate that KAE achieves substantial gains on state-of-the-art demographic prediction models.

User Identity Linkage is to find users belonging to the same real person in different social networks. Besides, anchor users refer to matching users known in advance. However, how to match users only based on network information is still very difficult and existing embedding methods suffer from the challenge of error propagation. Error propagation means the error occurring in learning some users’ embeddings may be propagated and amplified to other users along with edges in the network. In this paper, we propose the Anchor UseR ORiented Accordant Embedding (AURORAE) method to learn the vector representation for each user in each social network by capturing useful network information and avoiding error propagation. Specifically, AURORAE learns the potential relations between anchor users and all users, which means each user is directly connected to all anchor users and the error cannot be propagated without paths. Then, AURORAE captures the useful local structure information into final embeddings under the constraint of accordant vector representations between anchor users. Experimental results on real-world datasets demonstrate that our method significantly outperforms other state-of-the-art methods.

There is a significant style overfitting problem in traditional content supervision models of character recognition: insufficient generalization ability to recognize the characters with unseen font styles. To overcome this problem, in this paper we propose a novel framework named Style and Content Supervision (SCS) network, which integrates style and content supervision to resist style overfitting. Different from traditional models only supervised by content labels, SCS simultaneously leverages the style and content supervision to separate the task-specific features of style and content, and then mixes the style-specific and content-specific features using bilinear model to capture the hidden correlation between them. Experimental results prove that the proposed model is able to achieve the state-of-the-art performance on several widely used real world character sets, and it obtains relatively strong robustness when the size of training set is shrinking.

POI (point-of-interest) recommendation as an important location-based service has been widely utilized in helping people discover attractive locations. A variety of available check-in data provide a good opportunity for developing personalized POI recommender systems. However, the extreme sparsity of check-in data and inefficiency of exploiting unobserved feedback pose severe challenges for POI recommendation. To cope with these challenges, we develop a heterogeneous graph embedding-based personalized POI recommendation framework called HRec. It consists of two modules: the learning module and the ranking module. Specifically, we first propose the learning module to produce a series of intermediate feedback from unobserved feedback by learning the embeddings of users and POIs in the heterogeneous graph. Then we devise the ranking module to recommend each user the ultimate ranked list of relevant POIs by utilizing two pairwise feedback comparisons. Experimental results on two real-world datasets demonstrate the effectiveness and superiority of the proposed method.

Aligning users belonging to the same person in different social networks has attracted much attention. Recently, embedding methods have been proposed to represent users from different social networks into vector spaces with same dimension. To handle the challenge of vector space diversity, existing methods usually make vectors of known aligned users closer/consistent and overlap different vector spaces. However, compared to large amount of users in each social network, the consistence constraint on aligned users is not enough to reduce the diversity. Besides, missing edges/labels may provide incorrect information and affect the effect of the overlap between learned vector spaces. Therefore, we propose the OURLACER method, i.e, jOint UseR and LAbel ConsistencE Representation, to jointly represent each user and label under the consistence constraints of know aligned users and shared labels. Specifically, OURLACER utilizes collective matrix factorization to complete missing edges and labels for each user, which can provide sufficient information to distinguish each user. Moreover, OURLACER adds the consistence constraint on shared labels in different social networks. Because each user has own labels, label consistence can restrict each user and greatly reduce the diversity between learned vector spaces. Extensive experiments conducted on real-world datasets demonstrate that our method significantly outperforms other state-of-the-art methods.

Differential lipid metabolic requirements of sexually-mature males and females may influence the regulation of lipid metabolism-associated genes and hence the content of adipose tissue. We measured the expression of eight lipid metabolism-associated genes (fatty acid synthase, FASN; acylglycerol- 3- phosphate O-acyltransferase 9, AGPAT9; peroxisomal proliferator-activated receptor γ, PPARγ; lipoprotein lipase, LPL; carnitine palmitoyl transferase 1 A, CPT1A; carnitine palmitoyl transferase 1 B, CPT1B; acyl-COA dehydrogenase long chain, ACADL; monoglyceride lipase, MGL) in eight tissues (hypothalamus, HYP; liver; heart; pectoralis major muscle, PM; gastrocnemius muscle, GAS; abdominal fat, AF; clavicular fat, CF; subcutaneous fat, SF) of five male and five female white feather chickens using real time PCR at 217 d (when the females were at peak egg production). There were no difference between sexes, nor were there sex by tissue interactions for CPT1A and MGL. In both cases expression was greater for liver than the other tissues. When interactions of sex by tissue were significant, the FASN mRNA abundance in HYP, liver, and PM was greater for females than males. There was no sexual dimorphism for any tissue for PPARγ. Overall values were greater for adipose depots than HYP and liver with muscles intermediate for AGPAT9. LPL mRNA abundance in PM and AF was greater for females than males, with the pattern reversed for heart and SF. CPT1B mRNA abundance in GAS and CF was greater for females than males, with the relationship reversed for liver. ACADL mRNA abundance in HYP, liver, and GAS was greater for females than males, and lower in PM than males. The results demonstrated that expression of lipid metablism-associated genes varies among sexes in mature chickens depending on the gene and the tissue.

With the thriving of location-based social networks, a large number of user check-in data have been accumulated. Tasks such as the prediction of the next check-in location can be addressed through the usage of LBSN data. Previous work mainly uses the historical trajectories of users to analyze users' check-in behavior, while the social information of users was rarely used. In this paper, we propose a unified location prediction framework to integrate the effect of history check-in and the influence of social circles. We first employ the most frequent check-in model (MFC) and the user-based collaborative filtering model (UCF) to capture users' historical trajectories and users' implicit preference, respectively. Then we use the multi-social circle model (MSC) to model the influence of three social circles. Finally, we evaluate our location prediction framework in the real-world data sets, and the experimental results show that our model performs better than the state-of-the-art approaches in predicting the next check-in location.

User identity linkage is to identify all the users belonging to the same individual in different networks and has been widely studied along with the increasing popularity of diverse social media sites. Generally, a pair of probable corresponding users on different networks may form a true “Anchor Link”. Most existing methods identify a user based on unique features (username, interests, friends, etc.) and neglect the importance of users local network structure. Therefore, one challenging problem is how to address the user identity linkage problem if only structural information is available. In this paper, we explore techniques for dealing with the fundamental and accumulated information from neighbouring anchor links. Furthermore, we design a Trustworthy Predicting Approach (TPA) for computing the authority of an anchor link, inferring the trustworthiness of a candidate anchor link being true and predicting whether an anchor link is able to be veritably formed. Experiments illustrate the effectiveness of our proposed algorithm.

Recently, the convolutional neural networks (CNNs) show the great power in dealing with various image classification tasks. However, in the task of Chinese character recognition, there is a significant problem in CNN-based classifiers: insufficient generalization ability to recognize the Chinese characters with unfamiliar font styles. We call this problem the Style Overfitting. In the process of a human recognizing Chinese characters with various font styles, the internal skeletons of these characters are important indicators. This paper proposes a novel tool named Skeleton Kernel to capture skeleton features of Chinese characters. And we use it to assist CNN-based classifiers to prevent the Style Overfitting problem. Experimental results prove that our method firmly enhances the generalization ability of CNN-based classifiers. And compared to previous works, our method requires a small training set to achieve relatively better performance.

This study was to detect the expression of heart fatty acid-binding protein (H-FABP) and adipocyte fatty acid-binding protein (A-FABP) gene mRNA in different tissues of Rugao and Luyuan chickens at 56 d and 120 d by real-time fluorescence quantitative reverse transcription polymerase-chain reaction (FQ-RT-PCR). The primers were designed according to the sequences of H-FABP, A-FABP and GAPDH genes in Gallus gallus, which were used as target genes and internal reference gene, respectively. The levels of H-FABP and A-FABP gene expression were detected by SYBR Green I FQ-RT-PCR. The relative H-FABP and A-FABP gene mRNA expression level was calculated with 2 -ΔCt . Melting curve analysis showed a single peak of three genes. Intramuscular fat (IMF) content in breast muscle and leg muscle of the two chicken breeds at 120 d was higher than at 56 d. IMF content in breast muscle and leg muscle at 56 d and 120 d in Luyuan was significantly higher than in Rugao, however, abdominal fat of Luyuan was significantly lower than that of Rugao. The relative H-FABP gene mRNA expression level in cardiac muscle was the highest in both chicken breeds. The relative H-FABP and A-FABP gene expression of different tissues in Luyuan was higher than in Rugao. H-FABP gene mRNA expression had a negative effect on IMF of leg and breast muscles, and was significantly negatively correlated with IMF content. The relative A-FABP gene mRNA level in abdominal fat was higher than in liver. The A-FABP gene mRNA was not expressed in leg, breast and cardiac muscles. A-FABP gene mRNA expression level was significantly positively correlated with abdominal fat and had a significant effect on abdominal fat but not IMF content.

Interleukin-17 has been shown to be associated with autoimmune disease. The aim of the current study is to investigate the potential association of IL-17 polymorphisms with multiple sclerosis (MS) in Chinese Han patients. Two SNPs, rs763780 of IL-17F gene and rs2275913 of IL-17A gene were genotyped in 622 MS patients and 743 healthy controls by using a polymerase chain reaction-restriction fragment length polymorphism method (PCR-RFLP). Allele and genotype frequencies distribution of the two SNPs were examined between patients and controls using the Chi-Square test. All genotypic and allelic frequencies of the tested IL-17 polymorphisms in control cohort were in Hardy-Weinberg equilibrium. A significantly increased frequency of rs763780 TT genotype (corrected p value (Pc)=0.024, odds ratio=1.472, 95% CI=1.133-1.913) and T allele (corrected P (Pc)=0.018, odds ratio=1.446, 95% CI=1.134-1.844) was detected in MS patients compared with controls. The genotypic and allelic frequencies of rs2275913 in IL-17A gene were not different between patients with MS and controls. These results suggest that rs763780 is associated with multiple sclerosis in a Chinese Han population.