STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization.

Recent technological developments in spatial transcriptomics allow researchers to measure gene expression of cells and their spatial locations at the single-cell level, generating detailed biological insight into biological processes. A comprehensive database could facilitate the sharing of spatial transcriptomic data and streamline the data acquisition process for researchers. Here, we present the Spatial TranscriptOmics DataBase (STOmicsDB), a database that serves as a one-stop hub for spatial transcriptomics. STOmicsDB integrates 218 manually curated datasets representing 17 species. We annotated cell types, identified spatial regions and genes, and performed cell-cell interaction analysis for these datasets. STOmicsDB features a user-friendly interface for the rapid visualization of millions of cells. To further facilitate the reusability and interoperability of spatial transcriptomic data, we developed standards for spatial transcriptomic data archiving and constructed a spatial transcriptomic data archiving system. Additionally, we offer a distinctive capability of customizing dedicated sub-databases in STOmicsDB for researchers, assisting them in visualizing their spatial transcriptomic analyses. We believe that STOmicsDB could contribute to research insights in the spatial transcriptomics field, including data archiving, sharing, visualization and analysis. STOmicsDB is freely accessible at https://db.cngb.org/stomics/.

Chaotic phase noise-like encryption based on geometric shaping for coherent data center interconnections.

The network traffic of data centers (DCs) has increased unprecedentedly with the rapid development of digital economy. However, the data transmission faces security threats in the distributed optical interconnection and intensive interaction of DC networks. In this paper, we propose a chaotic phase noise-like encryption algorithm using geometric shaping (GS) for coherent DC interconnections (DCIs). A GS constellation is used to improve transmission performance, and it is combined with coherent equalization algorithms to improve security performance. Then, a chaotic encryption is designed based on phase noise-like transformation (PNLT). The data are effectively scrambled, and the confusion level of phase can be increased. Finally, 216 Gb/s 8-quadrature amplitude modulation (8-QAM) encrypted data are successfully verified on a 240 km transmission link of DCIs. The results show that this scheme can achieve a bit error rate (BER) performance gain of 1.1 dB and provide a highly compatible solution for realizing security enhanced DCIs.

Simultaneous removal of Cd(II) and As(V) by ferrihydrite-biochar composite: Enhanced effects of As(V) on Cd(II) adsorption.

The coexistence of cadmium (Cd(II)) and arsenate (As(V)) pollution has long been an environmental problem. Biochar, a porous carbonaceous material with tunable functionality, has been used for the remediation of contaminated soils. However, it is still challenging for the dynamic quantification and mechanistic understanding of the simultaneous sequestration of multi-metals in biochar-engineered environment, especially in the presence of anions. In this study, ferrihydrite was coprecipitated with biochar to investigate how ferrihydrite-biochar composite affects the fate of heavy metals, especially in the coexistence of Cd(II) and As(V). In the solution system containing both Cd(II) and As(V), the maximum adsorption capacities of ferrihydrite-biochar composite for Cd(II) and As(V) reached 82.03 µmol/g and 531.53 µmol/g, respectively, much higher than those of the pure biochar (26.90 µmol/g for Cd(II), and 40.24 µmol/g for As(V)) and ferrihydrite (42.26 µmol/g for Cd(II), and 248.25 µmol/g for As(V)). Cd(II) adsorption increased in the presence of As(V), possibly due to the changes in composite surface charge in the presence of As(V), and the increased dispersion of ferrihydrite by biochar. Further microscopic and mechanistic results showed that Cd(II) complexed with both biochar and ferrihydrite, while As(V) was mainly complexed by ferrihydrite in the Cd(II) and As(V) coexistence system. Ferrihydrite posed vital importance for the co-adsorption of Cd(II) and As(V). The different distribution patterns revealed by this study help to a deeper understanding of the behaviors of cations and anions in the natural environment.

Comparative Study on the Structural Properties and Bioactivities of Three Different Molecular Weights of Lycium barbarum Polysaccharides.

The molecular weight, the triple-helix conformation, the monosaccharide content, the manner of glycosidic linkages, and the polysaccharide conjugates of polysaccharides all affect bioactivity. The purpose of this study was to determine how different molecular weights affected the bioactivity of the Lycium barbarum polysaccharides (LBPs). By ethanol-graded precipitation and ultrafiltration membrane separation, one oligosaccharide (LBPs-1, 1.912 kDa) and two polysaccharides (LBPs-2, 7.481 kDa; LBPs-3, 46.239 kDa) were obtained from Lycium barbarum. While the major component of LBPs-1 and LBPs-2 was glucose, the main constituents of LBPs-3 were arabinose, galactose, and glucose. LBPs-2 and LBPs-3 exhibited triple-helix conformations, as evidenced by the Congo red experiment and AFM data. Sugar residues of LBPs-2 and LBPs-3 were elucidated by NMR spectra. The polysaccharides (LBPs-2 and LBPs-3) exhibited much higher antioxidant capacities than oligosaccharide (LBPs-1). LBPs-3 showed higher oxygen radical absorbance capacity (ORAC) and superoxide dismutase (SOD) activity than LBPs-2, but a lower capability for scavenging ABTS+ radicals. In zebrafish, LBPs-2 and LBPs-3 boosted the growth of T-lymphocytes and macrophages, enhanced the immunological response, and mitigated the immune damage generated by VTI. In addition to the molecular weight, the results indicated that the biological activities would be the consequence of various aspects, such as the monosaccharide composition ratio, the chemical composition, and the chemical reaction mechanism.

Downregulation of TRPC4 and TRPC5 Inhibits Smooth Muscle Cell Proliferation without Affecting Endothelial Cell Proliferation.

Aims: The main treatment for coronary heart disease is percutaneous coronary intervention (PCI), and drug-eluting stents are designed to inhibit vascular smooth muscle cell (VSMCs) proliferation and migration causing restenosis by releasing pharmacological agents into the vessel wall. Once drug-eluting stents are deployed, these pharmacological agents exert many biological effects in the coronary circulation, not only inhibition of VSMCs but also extension to vascular endothelial cells (VECs). The purpose of this study was to explore target molecules that inhibit VSMCs proliferation without affecting VECs. Methods: mRNA and protein expressions of transient receptor potential channels (TRPCs) in cultured VSMCs and VECs were determined by western blotting and RT-qPCR. VSMCs and VECs proliferation was evaluated using CCK-8 assays and western blotting of proliferating cell nuclear antigen (PCNA). Calcium backfilling assays were performed to detect intracellular calcium ion concentration in cultured VSMCs and VECs. Results: The TRPC6 expression was more abundant in VECs than VSMCs, while TRPC4 and TRPC5 expressions were more abundant in VSMCs than VECs. Knockdown of TRPC4 or TRPC5 alone had no remarkable inhibitory effect on VSMC proliferation. Synergistic knockdown of TRPC4 and TRPC5 inhibited the proliferation of VSMCs, declined the expression of the PCNA, and reduced the intracellular calcium ion concentration but not VECs. Conclusion: These data suggest that concurrent inhibition of TRPC4 and TRPC5 inhibits VSMCs proliferation without affecting VECs, thus providing novel targets for developing pharmacological agents for drug-eluting stents.

Corrigendum to "miR-221 Alleviates the Ox-LDL-Induced Macrophage Inflammatory Response via the Inhibition of DNMT3b-Mediated NCoR Promoter Methylation".

[This corrects the article DOI: 10.1155/2019/4530534.].

miR-221 Alleviates the Ox-LDL-Induced Macrophage Inflammatory Response via the Inhibition of DNMT3b-Mediated NCoR Promoter Methylation.

Atherosclerosis (AS) is a chronic inflammatory disease, and macrophages play a key role in all phases of AS. Recent studies have shown that miR-221 is a biomarker for AS and stroke; however, the role and mechanism of miR-221 in AS are unclear. Herein, we found that miR-221 and NCoR levels were decreased in ox-LDL-treated THP-1-derived macrophages. In contrast, DNMT3b, IL-6, and TNF-α expression levels were increased under these conditions. Upregulation of miR-221 or NCoR could partially inhibit ox-LDL-induced IL-6 and TNF-α expression. Further studies showed that DNMT3b was a target of miR-221. DNMT3b inhibition also suppressed IL-6 and TNF-α expression and increased NCoR expression in the presence of ox-LDL. Moreover, DNMT3b was involved in ox-LDL-induced DNA methylation in the promoter region of NCoR. These findings suggest that miR-221 suppresses ox-LDL-induced inflammatory responses via suppressing DNMT3b-mediated DNA methylation in the promoter region of NCoR. These results provide a rationale for using intracellular miR-211 as a possible antiatherosclerotic target.

Adherence to Moisturizing Subjects in Patient with Dry Skin in the Winter.

Dry skin in winter and moisturization are important topics. We included 72 patients with a diagnosis of dry skin in winter, and the patients were instructed to apply moisturizing products as part of their treatment plan from October 2017 to January 2018. We contacted the patients via telephone 2 weeks after the dermatologist appointment. The results from this study showed that patient adherence to dermatologist-recommended moisturizers is low. This study indicates that patients need more guidance in their dry skin treatment.

Personalized long-term prediction of cognitive function: Using sequential assessments to improve model performance.

Prediction of onset and progression of cognitive decline and dementia is important both for understanding the underlying disease processes and for planning health care for populations at risk. Predictors identified in research studies are typically accessed at one point in time. In this manuscript, we argue that an accurate model for predicting cognitive status over relatively long periods requires inclusion of time-varying components that are sequentially assessed at multiple time points (e.g., in multiple follow-up visits). We developed a pilot model to test the feasibility of using either estimated or observed risk factors to predict cognitive status. We developed two models, the first using a sequential estimation of risk factors originally obtained from 8 years prior, then improved by optimization. This model can predict how cognition will change over relatively long time periods. The second model uses observed rather than estimated time-varying risk factors and, as expected, results in better prediction. This model can predict when newly observed data are acquired in a follow-up visit. Performances of both models that are evaluated in10-fold cross-validation and various patient subgroups show supporting evidence for these pilot models. Each model consists of multiple base prediction units (BPUs), which were trained using the same set of data. The difference in usage and function between the two models is the source of input data: either estimated or observed data. In the next step of model refinement, we plan to integrate the two types of data together to flexibly predict dementia status and changes over time, when some time-varying predictors are measured only once and others are measured repeatedly. Computationally, both data provide upper and lower bounds for predictive performance.

The expression levels of chemotaxis-related molecules CXC chemokine receptor 1, interleukin-8, and pro-platelet basic protein in gingival tissues.

Background/purpose: Excessive host immune response is thought to be an important cause of periodontal tissue damage during periodontitis. The potent chemotaxis produced by locally released chemokines is the key signal to trigger this response. Here, we aimed to investigate the expression of CXC chemokine receptor 1 (CXCR1), and chemokines interleukin-8 (IL-8) and pro-platelet basic protein (PPBP) in human inflammatory gingival tissues compared with healthy tissues. Materials and methods: A total of 54 human gingival tissues, 27 healthy and 27 inflammatory samples, were collected. Fifteen specimens of each group were employed for quantitative reverse transcription polymerase chain reaction to determine the mRNA levels of CXCR1, IL-8, and PPBP. Six samples of each group were used for Western blotting to investigate the protein expression of CXCR1 and for enzyme-linked immunosorbent assay to evaluate the protein levels of IL-8 and PPBP, respectively. Results: The mRNA levels of chemokine receptor CXCR1, chemokine IL-8, and PPBP in inflammatory gingival tissues were significantly higher than those in healthy controls (P < 0.05). The protein levels of CXCR1, IL-8, and PPBP in inflammatory gingival tissues were also significantly higher than those in healthy gingival tissues (P < 0.05). Conclusion: When compared to healthy gingival tissues, the expression of CXCR1, IL-8, and PPBP in inflammatory gingival tissues is higher.

Sparse-representation algorithms for blind estimation of acoustic-multipath channels.

Acoustic channel estimation is an important problem in various applications. Unlike many existing channel estimation techniques that need known probe or training signals, this paper develops a blind multipath channel identification algorithm. The proposed approach is based on the single-input multiple-output model and exploits the sparse multichannel structure. Three sparse representation algorithms, namely, matching pursuit, orthogonal matching pursuit, and basis pursuit, are applied to the blind sparse identification problem. Compared with the classical least squares approach to blind multichannel estimation, the proposed scheme does not require that the channel order be exactly determined and it is robust to channel order selection. Moreover, the ill-conditioning induced by the large delay spread is overcome by the sparse constraint. Simulation results for deconvolution of both underwater and room acoustic channels confirm the effectiveness of the proposed approach.

VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild.

We present VoxelTrack for multi-person 3D pose estimation and tracking from a few cameras which are separated by wide baselines. It employs a multi-branch network to jointly estimate 3D poses and re-identification (Re-ID) features for all people in the environment. In contrast to previous efforts which require to establish cross-view correspondence based on noisy 2D pose estimates, it directly estimates and tracks 3D poses from a 3D voxel-based representation constructed from multi-view images. We first discretize the 3D space by regular voxels and compute a feature vector for each voxel by averaging the body joint heatmaps that are inversely projected from all views. We estimate 3D poses from the voxel representation by predicting whether each voxel contains a particular body joint. Similarly, a Re-ID feature is computed for each voxel which is used to track the estimated 3D poses over time. The main advantage of the approach is that it avoids making any hard decisions based on individual images. The approach can robustly estimate and track 3D poses even when people are severely occluded in some cameras. It outperforms the state-of-the-art methods by a large margin on four public datasets including Shelf, Campus, Human3.6 M and CMU Panoptic.

Effect of V/Mo Atomic Ratio on the Microstructure and Mechanical Properties of MoVCuN Coatings.

To improve the gas ionization ratio, the Mo-V-Cu-N coatings were deposited by pulsed dc magnetron sputtering with assistance from an anode layer ion source, and the influence of the V/Mo atomic ratio was explored with regard to the microstructure and mechanical properties of the coatings. The findings of this study indicated that the MoVCuN coatings exhibited a solid solution phase of FCC B1-MoVN with a prominent (220) preferred orientation, and the deposition rate was found to decrease from 4.7 to 1.8 nm/min when the V/Mo atomic ratio increased. The average surface roughness of the MoVCuN coatings gradually decreased, and the lowest surface roughness of 6.9 nm was achieved at a V/Mo atomic ratio of 0.31. Due to the enhanced ion bombardment effect, the coatings changed from a coarse columnar to a dense columnar crystal structure, and promoted grain refinement at higher V/Mo atomic ratios, contributing to a gradual improvement in the compressive residual stress, hardness and adhesion strength of the coatings.

Time reversal communication over doubly spread channels.

Conventional time reversal can mitigate multipath delay dispersion by temporal focusing. But it is not applicable to time-varying channels with a Doppler spread. Although recently time reversal communication has been adapted to time-variant channels, the modified technique requires frequent channel updates to track channel variations and cannot handle large Doppler spread, which means that it cannot achieve frequency focusing. In this paper, two time reversal receivers for underwater acoustic communications over doubly spread channels are proposed. The proposed approach, which can be interpreted as time-frequency channel matching, is based on the channel spreading function rather than impulse response adopted by the existing techniques; this leads to much less frequent channel updates. Unlike existing methods that only correct a single Doppler shift, the proposed approach uses a rake-like structure to compensate for multiple Doppler shifts and hence can eliminate severe Doppler spread induced by temporal channel variations. Simulation results verify the effectiveness of the proposed approach, indicating that it can simultaneously counteract delay and Doppler spreads, achieving both temporal and frequency focusing.

Fast estimation of sparse doubly spread acoustic channels.

The estimation of doubly spread underwater acoustic channels is addressed. By exploiting the sparsity in the delay-Doppler domain, this paper proposes a fast projected gradient method (FPGM) that can handle complex-valued data for estimating the delay-Doppler spread function of a time-varying channel. The proposed FPGM formulates the sparse channel estimation as a complex-valued convex optimization using an [script-l](1)-norm constraint. Conventional approaches to complex-valued optimization split the complex variables into their real and imaginary parts; this doubles the dimension compared with the original problem and may break the special data structure. Unlike the conventional methods, the proposed method directly handles the complex variables as a whole without splitting them into real numbers; hence the dimension will not increase. By exploiting the block Toeplitz-like structure of the coefficient matrix, the computational complexity of the FPGM is reduced to O(LNlogN), where L is the dimension of the Doppler shift and N is the signal length. Simulation results verify the accuracy and efficiency of the FPGM, indicating that is robust to parameter selection and is orders-of-magnitude faster than standard convex optimization algorithms. The Kauai experimental data processing results are also provided to demonstrate the performance of the proposed algorithm.

Dynamic Optimization Modeling of Smart Tourism Information System Using VRGIS in Big Data Environment.

The establishment of an intelligent, comprehensive, and all-encompassing information system for tourism management is the current trend in tourism informatization as a result of the continual development of modern information technology. Significant advancements in the field of VRGIS and its usage in research have been made as a result of the use of VRGIS to categorize, assess, plan, and manage tourism resources. The analysis of the recent development of VRGIS in tourism resource research is the first section of this work. This study examines and implements a mobile, computerized, and intelligent tourism service system that gives visitors a sense of the surrounding landscape using VRGIS. Three-dimensional mapping, environment detection, personal trajectory, and Weibo sharing are just a few of the system's many helpful features. While travelling, tourists can get services that are more intelligent and practical. The drawbacks of conventional geographic information systems include their reliance on sophisticated models, network limitations, and operational challenges. New software architecture is put in place to get rid of network restrictions, virtual reality peripherals are used to make operation more convenient, and system modeling is rebuilt using the TIN data model and model simplification. The results of experiments show improved refresh rates and peripheral expansion modules. The user experience is enhanced by this research.

Red Ghost Image Elimination Method Based on Driving Waveform Design in Three-Color Electrophoretic Displays.

Three-color electrophoretic displays (EPDs) are a new type of optoelectronic display device. However, they have the defect of red ghost images during gray scale transformation, which affects the accuracy of the gray scale display. In this paper, we proposed a new driving method for eliminating the red ghost images. A driving waveform was composed of an erasing stage, an activation stage, and a driving stage. First, the erasing stage was subdivided into a red erasing stage and an original erasing stage, the red erasing stage was used to eliminate residual red particles in the top of the microcapsules. Then, a high-frequency square wave was used as the activation stage for increasing the activity of the black and white particles. Meanwhile, the intensity of flickers could be decreased by the high-frequency square wave. Finally, the performance of the driving waveform was tested by a colorimeter. The experimental results showed that the driving waveform could effectively eliminate red ghost images by 80.43% and reduce the flicker intensity by 79.63%, compared with an existing driving waveform.

Neighborhood Geometric Structure-Preserving Variational Autoencoder for Smooth and Bounded Data Sources.

Many data sources, such as human poses, lie on low-dimensional manifolds that are smooth and bounded. Learning low-dimensional representations for such data is an important problem. One typical solution is to utilize encoder-decoder networks. However, due to the lack of effective regularization in latent space, the learned representations usually do not preserve the essential data relations. For example, adjacent video frames in a sequence may be encoded into very different zones across the latent space with holes in between. This is problematic for many tasks such as denoising because slightly perturbed data have the risk of being encoded into very different latent variables, leaving output unpredictable. To resolve this problem, we first propose a neighborhood geometric structure-preserving variational autoencoder (SP-VAE), which not only maximizes the evidence lower bound but also encourages latent variables to preserve their structures as in ambient space. Then, we learn a set of small surfaces to approximately bound the learned manifold to deal with holes in latent space. We extensively validate the properties of our approach by reconstruction, denoising, and random image generation experiments on a number of data sources, including synthetic Swiss roll, human pose sequences, and facial expression images. The experimental results show that our approach learns more smooth manifolds than the baselines. We also apply our approach to the tasks of human pose refinement and facial expression image interpolation where it gets better results than the baselines.

Red Display for Three-Color Electrophoretic Displays with High Saturation via a Separation Stage between Black and Red Particles.

A three-color electrophoretic display (EPD) can solve the defect of an insufficient color display of black/white EPDs, but it is difficult to achieve a high red saturation due to the same driving polarity between black and red electrophoretic particles. In this work, a separation stage was proposed in the driving process to increase the red saturation in three-color EPDs. Firstly, red particles' motion was analyzed by the electrophoretic theory and Stokes' theorem to optimize driving parameters. Secondly, the activity of black particles was analyzed by testing different driving process parameters, and an optimal activation parameter for red particles was obtained. Next, the separation stage parameters were analyzed to reduce the mixing degree of black and red electrophoretic particles. Experimental results showed that the red and black electrophoretic particles could be effectively separated. Compared with an existing driving method, the red saturation was increased by 23.4%.

APANet: Auto-Path Aggregation for Future Instance Segmentation Prediction.

Despite the remarkable progress achieved in conventional instance segmentation, the problem of predicting instance segmentation results for unobserved future frames remains challenging due to the unobservability of future data. Existing methods mainly address this challenge by forecasting features of future frames. However, these methods always treat features of multiple levels (e.g., coarse-to-fine pyramid features) independently and do not exploit them collaboratively, which results in inaccurate prediction for future frames; and moreover, such a weakness can partially hinder self-adaption of a future segmentation prediction model for different input samples. To solve this problem, we propose an adaptive aggregation approach called Auto-Path Aggregation Network (APANet), where the spatio-temporal contextual information obtained in the features of each individual level is selectively aggregated using the developed "auto-path". The "auto-path" connects each pair of features extracted at different pyramid levels for task-specific hierarchical contextual information aggregation, which enables selective and adaptive aggregation of pyramid features in accordance with different videos/frames. Our APANet can be further optimized jointly with the Mask R-CNN head as a feature decoder and a Feature Pyramid Network (FPN) feature encoder, forming a joint learning system for future instance segmentation prediction. We experimentally show that the proposed method can achieve state-of-the-art performance on three video-based instance segmentation benchmarks for future instance segmentation prediction.