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Background and Aims: MicroRNAs (miRNAs) play important roles in hepatocyte differentiation from human bone marrow mesenchymal stem cells (hBMSCs) and the therapeutic application in vivo. However, the mechanisms of miRNA regulation are still unknown. This study aimed to profile the miRNA basis for improving the function of hBMSC-differentiated hepatocyte-like cells (hBMSC-Heps). Methods: Characteristic miRNAs of hBMSC-Heps were identified by transcriptome sequencing and validated by quantitative real-time polymerase chain reaction (qRT-PCR). An in vivo hBMSC transplantation model was used to assess the regulatory effects of miRNAs on liver regeneration during hBMSC therapy in pigs with fulminant hepatic failure (FHF). The biological functions of significant miRNA molecules were confirmed by transfection of miRNA activators or inhibitors into hBMSCs during hepatogenic differentiation. Results: The transcriptome of hBMSC-Heps showed characteristics distinct from those of undifferentiated hBMSCs. A total of 77 miRNAs were significantly differentially expressed in hBMSC-Heps at day 10 and day 20 after hBMSC differentiation that were directly related to the functions of hepatocytes. Among the top 10 significantly differentially expressed and the top 10 most abundant miRNAs, nine miRNAs that exhibited a pattern of gradual change were chosen for further analysis. The expression of nine miRNAs was confirmed by qRT-PCR in vitro and showed the same changing trends in vivo in an hBMSC transplantation model in pigs. Functional experiments with these miRNAs showed that activators of hsa-miR-26b-5p and hsa-miR-148a-3p and an inhibitor of hsa-miR-423-3p were sufficient to improve the differentiation of hBMSCs into hepatocyte-like cells. Conclusions: Transcriptome profiles of miRNA revealed the basis of the differentiation and development of hBMSC-Heps. Manipulation of three miRNAs (hsa-miR-26b-5p, hsa-miR-148a-3p and hsa-miR-423-3p) significantly improved hepatocyte generation and liver regeneration, indicating the potential of these miRNAs for future clinical applications.

In this study, we explored the source-sink characteristics of methyl halide (CH

BackgroundDifferential diagnosis of patients with suspected infections is particularly difficult, but necessary for prompt diagnosis and rational use of antibiotics. A substantial proportion of these patients have non-infectious diseases that include malignant tumors. This study aimed to explore the clinical value of metagenomic next-generation sequencing (mNGS) for tumor detection in patients with suspected infections.MethodsA multicenter, prospective case study involving patients diagnosed with suspected infections was conducted in four hospitals in Shanghai, China between July 2019 and January 2020. Based upon mNGS technologies and chromosomal copy number variation (CNV) analysis on abundant human genome, a new procedure named Onco-mNGS was established to simultaneously detect pathogens and malignant tumors in all of the collected samples from patients.ResultsOf 140 patients screened by Onco-mNGS testing, 115 patients were diagnosed with infections; 17 had obvious abnormal CNV signals indicating malignant tumors that were confirmed clinically. The positive percent agreement and negative percent agreement of mNGS testing compared to clinical diagnosis was 53.0% (61/115) and 60% (15/25), vs. 20.9% (24/115) and 96.0% (24/25), respectively, for conventional microbiological testing (both P <0.01). Klebsiella pneumoniae (14.8%, 9/61) was the most common pathogen detected by mNGS, followed by Escherichia coli (11.5%, 7/61) and viruses (11.5%, 7/61). The chromosomal abnormalities of the 17 cases included genome-wide variations and local variations of a certain chromosome. Five of 17 patients had a final confirmed with malignant tumors, including three lung adenocarcinomas and two hematological tumors; one patient was highly suspected to have lymphoma; and 11 patients had a prior history of malignant tumor.ConclusionThis preliminary study demonstrates the feasibility and clinical value of using Onco-mNGS to simultaneously search for potential pathogens and malignant tumors in patients with suspected infections.

Commuter flow is an important part of metro passenger flow. The aim of this study is to develop an efficient and effective method to identify the spatiotemporal commuting patterns of Metro Line 2 in Hangzhou. Using one-week transit smart card data and a questionnaire survey of Metro Line 2, the authors distinguished the spatiotemporal regularity of individual commuters, including first travel time, last travel time, and the number of travelling days on weekdays. This data could be used to identify transit commuters by leveraging ensemble learning approaches. The random forest algorithm was adopted as a low-cost, high-efficiency analysis method, and the classification model was established with the information of travel time, days of travelling, and the unique tag information in the questionnaire survey data. Then, numerical tests were carried out to show that the Precision and Recall rates of the proposed model could reach as high as 0.96 and 0.92, respectively. Finally, the validated random forest model was applied to identify metro commuters from the smartcard data. The results show that less than one-third of passengers are commuter traffic and are mainly concentrated during peak hours. These extracted personal-level commute models can be used as valuable information for the design and optimisation of public transportation networks.

Background: Human bone marrow mesenchymal stem cell-derived hepatocyte-like cells (hBMSC-HLCs) are a promising alternative for primary human hepatocytes (HHs) for treating liver disease. However, the molecular characteristics of HLCs remain unclear. Here, we aimed to clarify the transcriptome characteristics of hBMSC-HLCs for future clinical application. Materials and Methods: hBMSCs were isolated from the bone marrow of healthy volunteers and differentiated into hepatocytes. mRNA sequencing was used in the transcriptome profiling of hBMSC-HLCs, with hBMSCs and HHs as controls. Results: hBMSC-HLCs exhibited a polygonal morphology, glycogen accumulation and albumin expression. A total of 630 upregulated and 1082 downregulated genes were observed in hBMSC-HLCs and HHs compared with undifferentiated hBMSCs. The upregulated genes were mainly involved in hepatic metabolism and inflammatory and immune responses. The downregulated genes were mainly associated with stem cell characteristics (multipotent differentiation, cell cycle regulation, etc.). Confirmatory qRT-PCR of 9 upregulated and 9 downregulated genes with log2 fold changes > 5 showed similar results. In vivo transdifferentiation of hBMSCs in pigs with fulminant hepatic failure confirmed the similarly upregulated expression of 5 hepatogenic genes (TDO2, HP, SERPINA3, LBP and SAA1), showing a 150-fold change in liver tissues at 7 days after hBMSC transplantation. These 5 genes mainly contributed to liver metabolism and inflammation. Conclusion: hBMSC-HLCs possess a hepatic transcriptome profile and express hepatic-specific genes in vitro and in vivo, which might be useful for future clinical applications. The five upregulated genes identified herein could be potential biomarkers for the characterization of hBMSC-HLCs.

Quantitative evaluation of spatial brightness has been difficult, mainly due to the lack of a metric that is both highly related to subjective evaluation and convenient to measure in the field. This work investigated the applicability of using indirect corneal illuminance to evaluate spatial brightness for a visual field in interior spaces. Three lighting scenes with different patterns of lighting distribution, which all delivered indirect light to the subjects, were compared against each other in pairs for spatial brightness. The corresponding indirect corneal illuminance required for each test scene to match the spatial brightness of the reference scene with a fixed corneal illuminance was obtained. The results showed that our proposed metric had a high correlation with subjective evaluation of spatial brightness even under very different patterns of lighting distribution. Furthermore, the proposed metric was compared with the prior metrics of MRSE and Lav,B40 in spatial brightness evaluation, and the former showed the best correlation with subjective judgments. Since the spatial brightness assessment for various visual fields together compose people’s overall impression of an illuminated space, the proposed metric of indirect corneal illuminance, which combines both accuracy and convenience in measurement, could serve as a preferred metric for spatial brightness evaluation.

In this paper, we present an Efficient Planning System for automated vehicles In highLy interactive envirONments (EPSILON). EPSILON is an efficient interaction-aware planning system for automated driving, and is extensively validated in both simulation and real-world dense city traffic. It follows a hierarchical structure with an interactive behavior planning layer and an optimization-based motion planning layer. The behavior planning is formulated from a partially observable Markov decision process (POMDP), but is much more efficient than naively applying a POMDP to the decision-making problem. The key to efficiency is guided branching in both the action space and observation space, which decomposes the original problem into a limited number of closed-loop policy evaluations. Moreover, we introduce a new driver model with a safety mechanism to overcome the risk induced by the potential imperfectness of prior knowledge. For motion planning, we employ a spatio-temporal semantic corridor (SSC) to model the constraints posed by complex driving environments in a unified way. Based on the SSC, a safe and smooth trajectory is optimized, complying with the decision provided by the behavior planner. We validate our planning system in both simulations and real-world dense traffic, and the experimental results show that our EPSILON achieves human-like driving behaviors in highly interactive traffic flow smoothly and safely without being over-conservative compared to the existing planning methods., Accepted by the IEEE Transactions on Robotics (T-RO)

Background Metagenomic next-generation sequencing (mNGS) offers the promise of unbiased detection of emerging pathogens. However, in indexed sequencing, the sequential paradigm of data acquisition, demultiplexing, and analysis restrain read assignment in advance and real-time analysis, resulting in lengthy turnaround time for clinical metagenomic detection. Methods We described the utility of internal-index adaptors with different lengths of barcode in multiplex sequencing. The base composition for each position within these adaptors was well-balanced to ensure nucleotide diversity and optimal sequencing performance and to achieve the early assignment of reads by first sequencing the barcodes. Combined with an automated library preparation device, we delivered a rapid and real-time bioinformatics pathogen identification solution for the Illumina NextSeq platform. The diagnostic performance was evaluated by testing 153 lower respiratory tract specimens using mNGS in comparison to culture, 16S/internal transcribed spacer amplicon sequencing, and additional PCR-based tests. Results By calculating the average F1 scores of all read lengths under different threshold values, we established the optimal threshold for pathogens identification, and found that 36 bp was the optimal shortest read length for rapid mNGS analysis. Rapid detection had a negative percentage agreement and positive percentage agreement of 100% and 85.1% for bacteria and 97.4% and 80.3% for fungi, when compared to a composite standard. The rapid mNGS solution enabled accurate pathogen identification in about 9.1 to 10.1 h sample-to-answer turnaround time. Conclusions Optimized internal index adaptors combined with a real-time analysis pipeline provide a potential tool for a first-line test in critically ill patients.

Supplemental Files and Data. Raw data for the copy numbers of chromosomewere shown in fd.txt format. The picture of genomic instability of each sample was shown in png format

Recently, physical layer (PHY) caching has been proposed to exploit the dynamic side information induced by caches at base stations (BSs) to support coordinated multi-point (CoMP) and achieve high degrees of freedom (DoF) gains. Due to the limited cache storage capacity, the performance of PHY caching depends heavily on the cache content placement algorithm. In the existing algorithms, the cache content placement is adaptive to the long-term popularity distribution in an offline manner. We propose an online PHY caching framework, which adapts the cache content placement to microscopic spatial and temporary popularity variations to fully exploit the benefits of PHY caching. Specifically, the joint optimization of online cache content placement and content delivery is formulated as a mixed-timescale drift minimization problem to increase the CoMP opportunity and reduce the cache content placement cost. We propose a low-complexity algorithm to obtain a throughput-optimal solution. Moreover, we provide a closed-form characterization of the maximum sum DoF in the stability region and study the impact of key system parameters on the stability region. The simulations results show that the proposed online PHY caching framework achieves large gain over existing solutions.

The demand for parking spaces in city centers often exceeds the supply. The spatial distribution of such spaces is uneven, which leads to the overheating of hot parking lots. To balance existing conflicts, parking charge and reservation mechanisms are usually utilized to improve the overall efficiency of parking spaces. This study aims to analyze and compare the benefits of these instruments and provide operational suggestions for urban parking managers. To achieve this goal, we introduce an agent simulation system and construct a simulation framework of parking charge and reservation. Then, the average travel time and total parking space utilization time are combined as a comprehensive benefit with different weights, and the benefits of parking charge and reservation are analyzed and compared under the corresponding weight. Parking charge and reservation are considered comprehensively in a combined simulation example. Results show that parking charge and reservation can remarkably improve the comprehensive benefit, and parking charge can achieve the maximum comprehensive benefit when the fee is high. When the fee is low, such as less than 26 yuan/h, the reservation mechanism can achieve a good level of benefit. Thus, parking reservation in most cities in China is an effective means to manage popular parking lots when the parking fee is low and difficult to increase.

Fast and accurate depth estimation, or stereo matching, is essential in embedded stereo vision systems, requiring substantial design effort to achieve an appropriate balance among accuracy, speed and hardware cost. To reduce the design effort and achieve the right balance, we propose FP-Stereo for building high-performance stereo matching pipelines on FPGAs automatically. FP-Stereo consists of an open-source hardware-efficient library, allowing designers to obtain the desired implementation instantly. Diverse methods are supported in our library for each stage of the stereo matching pipeline and a series of techniques are developed to exploit the parallelism and reduce the resource overhead. To improve the usability, FP-Stereo can generate synthesizable C code of the FPGA accelerator with our optimized HLS templates automatically. To guide users for the right design choice meeting specific application requirements, detailed comparisons are performed on various configurations of our library to investigate the accuracy/speed/cost trade-off. Experimental results also show that FP-Stereo outperforms the state-of-the-art FPGA design from all aspects, including 6.08% lower error, 2x faster speed, 30% less resource usage and 40% less energy consumption. Compared to GPU designs, FP-Stereo achieves the same accuracy at a competitive speed while consuming much less energy., IEEE International Conference on Field Programmable Logic and Applications (FPL), 2020

It is critical to predict the motion of surrounding vehicles for self-driving planning, especially in a socially compliant and flexible way. However, future prediction is challenging due to the interaction and uncertainty in driving behaviors. We propose planning-informed trajectory prediction (PiP) to tackle the prediction problem in the multi-agent setting. Our approach is differentiated from the traditional manner of prediction, which is only based on historical information and decoupled with planning. By informing the prediction process with the planning of the ego vehicle, our method achieves the state-of-the-art performance of multi-agent forecasting on highway datasets. Moreover, our approach enables a novel pipeline which couples the prediction and planning, by conditioning PiP on multiple candidate trajectories of the ego vehicle, which is highly beneficial for autonomous driving in interactive scenarios.

Decision-making in dense traffic scenarios is challenging for automated vehicles (AVs) due to potentially stochastic behaviors of other traffic participants and perception uncertainties (e.g., tracking noise and prediction errors, etc.). Although the partially observable Markov decision process (POMDP) provides a systematic way to incorporate these uncertainties, it quickly becomes computationally intractable when scaled to the real-world large-size problem. In this paper, we present an efficient uncertainty-aware decision-making (EUDM) framework, which generates long-term lateral and longitudinal behaviors in complex driving environments in real-time. The computation complexity is controlled to an appropriate level by two novel techniques, namely, the domain-specific closed-loop policy tree (DCP-Tree) structure and conditional focused branching (CFB) mechanism. The key idea is utilizing domain-specific expert knowledge to guide the branching in both action and intention space. The proposed framework is validated using both onboard sensing data captured by a real vehicle and an interactive multi-agent simulation platform. We also release the code of our framework to accommodate benchmarking., Comment: Accepted by IEEE International Conference on Robotics and Automation (ICRA) 2020

Background Lung biopsy tissue samples can be used for infection detection and cancer diagnosis. Metagenomic next-generation sequencing (mNGS) has the potential to further improve diagnosis. Methods From July 2018 to May 2020, lung biopsy samples of 133 patients with suspected pulmonary infection or abnormal imaging findings were collected and subjected to clinical microbiological testing, Illumina and Nanopore sequencing to identify pathogens. The neural networks were pretrained by extracting features of human reads from 2,095 metagenomic next-generation sequencing results, and the human reads of lung biopsy samples were entered into the validated pipeline to predict the risk of cancer. Findings Based on the pathogen-cancer detection pipeline, the Illumina platform showed 77·6% sensitivity and 97·6% specificity compared to the composite reference standard for infection diagnosis. However, the Nanopore platform showed 34·7% sensitivity and 98·7% specificity. mNGS identified more fungi, which was confirmed by subsequent pathological examination. M. tuberculosis complex was weakly detected. For cancer detection, compared with histology, the Illumina platform showed 83·7% sensitivity and 97·6% specificity, diagnosing an additional 36 cancer patients, of whom half had abnormal imaging findings (pulmonary shadow, space-occupying lesions, or nodules). Interpretation For the first time, we have established a pipeline to simultaneously detect pathogens and cancer based on Illumina sequencing of lung biopsy tissue. This pipeline efficiently diagnosed cancer in patients with abnormal imaging findings. Funding This work was supported by the National Key Research and Development Program of China and National Natural Science Foundation of China.

Planning safe trajectories for autonomous vehicles in complex urban environments is challenging since there are numerous semantic elements (such as dynamic agents, traffic lights and speed limits) to consider. These semantic elements may have different mathematical descriptions such as obstacle, constraint and cost. It is non-trivial to tune the effects from different combinations of semantic elements for a stable and generalizable behavior. In this paper, we propose a novel unified spatio-temporal semantic corridor (SSC) structure, which provides a level of abstraction for different types of semantic elements. The SSC consists of a series of mutually connected collision-free cubes with dynamical constraints posed by the semantic elements in the spatio-temporal domain. The trajectory generation problem then boils down to a general quadratic programming (QP) formulation. Thanks to the unified SSC representation, our framework can generalize to any combination of semantic elements. Moreover, our formulation provides a theoretical guarantee that the entire trajectory is safe and constraint-satisfied, by using the convex hull and hodograph properties of piecewise Bezier curve parameterization. We also release the code of our method to accommodate benchmarking., Accepted by IEEE Robotics and Automation Letters (IEEE RA-L)