Computational cytometer based on magnetically modulated coherent imaging and deep learning.

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications

The complete chloroplast genome sequence of the Manglietia longirostrata Sima, a rare and endemic species to china

Manglietia longirostrata Sima is a rare and endemic species in China. The complete chloroplast genome (cpDNA) of M. longirostrata was sequenced and assembled in this study. The cpDNA is 160,049 bps in length, contains a large single-copy region (LSC) of 88,098 bp and a small single-copy region (SSC) of 18,861 bp, separated by a pair of identical inverted repeat (IR) regions of 26,571 bp, each. The genome contains 123 genes, including 73 protein-coding genes, 8 ribosomal RNA genes, and 37 transfer RNA genes. Phylogenetic analysis of cp genome of M. longirostrata with 11 chloroplast genomes previously reported in the Magnoliaceae shows that M. longirostrata is close to Manglietia megaphylla with high bootstrap value

A Hardware-Friendly Optical Flow-Based Time-to-Collision Estimation Algorithm

This work proposes a hardware-friendly, dense optical flow-based Time-to-Collision (TTC) estimation algorithm intended to be deployed on smart video sensors for collision avoidance. The algorithm optimized for hardware first extracts biological visual motion features (motion energies), and then utilizes a Random Forests regressor to predict robust and dense optical flow. Finally, TTC is reliably estimated from the divergence of the optical flow field. This algorithm involves only feed-forward data flows with simple pixel-level operations, and hence has inherent parallelism for hardware acceleration. The algorithm offers good scalability, allowing for flexible tradeoffs among estimation accuracy, processing speed and hardware resource. Experimental evaluation shows that the accuracy of the optical flow estimation is improved due to the use of Random Forests compared to existing voting-based approaches. Furthermore, results show that estimated TTC values by the algorithm closely follow the ground truth. The specifics of the hardware design to implement the algorithm on a real-time embedded system are laid out

Evaluation of Groundwater Using an Integrated Approach of Entropy Weight and Stochastic Simulation: A Case Study in East Region of Beijing

Groundwater is an important source of water in Beijing. Hydrochemical composition and water quality are the key factors to determine the availability of groundwater. Therefore, an improved integrated weight water quality index approach (IWQI) combining the entropy weight method and the stochastic simulation method is proposed. Through systematic investigation of groundwater chemical composition in different periods, using a hydrogeochemical diagram, multivariate statistics and spatial interpolation analysis, the spatial evolution characteristics and genetic mechanism of groundwater chemistry are discussed. The results show that the groundwater in the study area is weakly alkaline and low mineralized water. The south part of the study area showed higher concentrations of total dissolved solids, total hardness and NO3−-N in the dry season and wet season, and the main hydrochemical types are HCO3−-Ca and HCO3−-Ca-Mg. The natural source mechanism of the groundwater chemical components in Chaoyang District includes rock weathering, dissolution and cation exchange, while the human-made sources are mainly residents and industrial activities. Improved IWQI evaluation results indicate that water quality decreases from southwest to northeast along groundwater flow path. The water quality index (WQI) method cannot reflect the trend of groundwater. Sensitivity analysis indicated that the improved IWQI method could describe the overall water quality reliably, accurately and stably

The complete chloroplast genome sequence of Magnolia delavayi (Magnoliaceae), a rare ornamental and medical tree endemic to China

Magnolia delavayi is a rare, famous ornamental and important medical tree endemic to China. Here, we assembled the complete chloroplast (cp) genome sequence of M. delavayi. Its length is 159,715 bp with four sub-regions: 87,906 bp of large single-copy region and 18,761 bp of small single-copy region are separated by two inverted repeats regions, each 26,524 bp. The genome contains 77 protein-coding genes, 6 rRNAs, and 29 tRNAs genes. Phylogenetic analysis of cp genome of M. delavayi with previously reported chloroplast genomes in Magnolia shows that M. delavayi is close to M. odoratissima with high bootstrap value

Can early cited proportion in certain sections reflect the future impact of the scientific articles?

Citations in scientific publications are a vehicle for the dissemination of knowledge and can reflect the sources and bases of scientific research. The citation location study suggests that citations in different parts of the scientific literature have different functions and values. To some extent, the number of citations or the frequency of citations based on citation location better reflects the true impact of a paper. In this respect, our study aimed to explore whether the early cited proportion in specific sections of scientific articles can reflect their impact in the future. Firstly, we obtained full-text data on Alzheimer's disease from the PubMed central open-access subset and extracted citation data. We then recognized the partitions of articles through a combination of rule matching and argumentative zoning recognition models. Finally, we conducted Spearman's test, and results show that the early proportion of citations in result and method section is positively correlated with total citations, whereas this pattern did not exist in sections such as the introduction. The results demonstrate that in Alzheimer's disease articles, the citations in the methods and results sections better reflect the impact of the article. Our findings could provide some new insights into citation function identification and paper impact prediction