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Umami taste receptor type 1 member 1/3 (T1R1/T1R3) heterodimer has multiple ligand-binding sites, most of which are located in T1R1

Short-term passenger flow forecasting is an essential component in urban rail transit operation. Emerging deep learning models provide good insight into improving prediction precision. Therefore, we propose a deep learning architecture combining the residual network (ResNet), graph convolutional network (GCN), and long short-term memory (LSTM) (called "ResLSTM") to forecast short-term passenger flow in urban rail transit on a network scale. First, improved methodologies of the ResNet, GCN, and attention LSTM models are presented. Then, the model architecture is proposed, wherein ResNet is used to capture deep abstract spatial correlations between subway stations, GCN is applied to extract network topology information, and attention LSTM is used to extract temporal correlations. The model architecture includes four branches for inflow, outflow, graph-network topology, as well as weather conditions and air quality. To the best of our knowledge, this is the first time that air-quality indicators have been taken into account, and their influences on prediction precision quantified. Finally, ResLSTM is applied to the Beijing subway using three time granularities (10, 15, and 30 min) to conduct short-term passenger flow forecasting. A comparison of the prediction performance of ResLSTM with those of many state-of-the-art models illustrates the advantages and robustness of ResLSTM. Moreover, a comparison of the prediction precisions obtained for time granularities of 10, 15, and 30 min indicates that prediction precision increases with increasing time granularity. This study can provide subway operators with insight into short-term passenger flow forecasting by leveraging deep learning models., in IEEE Transactions on Intelligent Transportation Systems

Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies of roadway links and the dynamic temporal patterns of traffic states make it particularly challenging. To address these challenges, we propose a new capsule network (CapsNet) to extract the spatial features of traffic networks and utilize a nested LSTM (NLSTM) structure to capture the hierarchical temporal dependencies in traffic sequence data. A framework for network-level traffic forecasting is also proposed by sequentially connecting CapsNet and NLSTM. On the basis of literature review, our study is the first to adopt CapsNet and NLSTM in the field of traffic forecasting. An experiment on a Beijing transportation network with 278 links shows that the proposed framework with the capability of capturing complicated spatiotemporal traffic patterns outperforms multiple state-of-the-art traffic forecasting baseline models. The superiority and feasibility of CapsNet and NLSTM are also demonstrated, respectively, by visualizing and quantitatively evaluating the experimental results.

Background β-Farnesene is a sesquiterpene with versatile industrial applications. The production of β-farnesene from waste lipid feedstock is an attractive method for sustainable production and recycling waste oil. Yarrowia lipolytica is an unconventional oleaginous yeast, which can use lipid feedstock and has great potential to synthesize acetyl-CoA-derived chemicals. Results In this study, we engineered Y. lipolytica to produce β-farnesene from lipid feedstock. To direct the flux of acetyl-CoA, which is generated from lipid β-oxidation, to β-farnesene synthesis, the mevalonate synthesis pathway was compartmentalized into peroxisomes. β-Farnesene production was then engineered by the protein engineering of β-farnesene synthase and pathway engineering. The regulation of lipid metabolism by enhancing β-oxidation and eliminating intracellular lipid synthesis was further performed to improve the β-farnesene synthesis. As a result, the final β-farnesene production with bio-engineering reached 35.2 g/L and 31.9 g/L using oleic acid and waste cooking oil, respectively, which are the highest β-farnesene titers reported in Y. lipolytica. Conclusions This study demonstrates that engineered Y. lipolytica could realize the sustainable production of value-added acetyl-CoA-derived chemicals from waste lipid feedstock.

IntroductionEndothelial cells play important roles in neurodegenerative diseases caused by diabetes, therefore, we aimed at investigating the mechanisms through which endothelial cells are involved in diabetes development.MethodsSingle cell analysis was performed to identify the major endothelial cell subtypes in cardiovascular tissues that are involved in diabetes development. A cell-cell communication approach was then used to identify ligand-receptor interaction pairs between these cell types. Differential expression analysis between the two experimental groups [standard chow diet group and diabetogenic diet with cholesterol (DDC) group] was used to identify diabetes-related differentially expressed genes (DEGs). The upregulated genes were used to identify candidate ligands or receptors, as well as the corresponding cell types. Cell trajectory inference was performed to identify the stage of cell development and changes in expression of candidate ligands or receptors during cell development. Gene set enrichment analysis (GSEA) was conducted to investigate the biological functions of genes of purpose. Finally, molecular dynamics simulations (MDSs) were used to predict potential drugs with the ability to target the proteins of purpose.ResultsSeven cell types, including five endothelial cell subtypes (EC_1, EC_2, EC_3, EC_4, and EC_EndMT), were identified from endothelial cell-enriched single cell samples from the heart and aorta of mice. Cell-cell communication analysis revealed the potential ligand-receptor interactions between these cell types while five important ligand-receptor-associated genes, including Fn1, Vcam1, Fbn1, Col4a1, and Col4a2, were established by differential expression analysis. Among them, Vcam1 is mainly expressed in EC_EndMT and is involved in interactions between EC_EndMT and other cells. Cell trajectory extrapolation analysis revealed a shift from EC_2/EC_4 to EC_EndMT and a shift from EC_EndMT to EC_3/EC_1 during the progression of diabetes. GSEA analysis revealed that upregulation of VCAM1 may have inhibitory effects on cell growth and energy metabolism.ConclusionEC_EndMT subtypes have a complex role in neurodegenerative diseases caused by diabetes. Through mechanisms involved in cell-cell communication, Vcam1 may play an important role in dysregulation of biological functions of EC_ EndMT. Molecular docking results of the quercetin-VCAM1 complex suggest that quercetin may be an effective drug for targeting this protein.

Episomal plasmids are crucial expression tools for recombinant protein production and genome editing. In Saccharomyces cerevisiae, 2-μm artificial plasmids with a high copy number have been developed and used in metabolic engineering and synthetic biology. However, in unconventional yeasts such as Yarrowia lipolytica, episomal expression relies on a chromosome replication system; this system has the disadvantages of genetic instability and low copy numbers. In this study, we identified and characterized replication origins from the mitochondrial DNA (mtDNA) of Y. lipolytica. A 516-bp mtDNA sequence, mtORI, was confirmed to mediate the autonomous replication of circular plasmids with high protein expression levels and hereditary stability. However, the nonhomologous end-joining pathway could interfere with mtORI plasmid replication and engender genetic heterogeneity. In the Po 1f ΔKu70 strain, the homogeneity of the mtORI plasmid was significantly improved, and the highest copy number reached 5.0 per cell. Overall, mitochondrial-origin sequences can be used to establish highly stable and autonomously replicating plasmids, which can be a powerful supplement to the current synthetic biology tool library and promote the development of Y. lipolytica as a microbial cell factory.

Taste peptides with umami/bitterness play a role in food attributes. However, the taste mechanisms of peptides are not fully understood, and the identification of these peptides is time-consuming. Here, we created a taste peptide database by collecting the reported taste peptide information. Eight key molecular descriptors from di/tri-peptides were selected and obtained by modeling screening. A gradient boosting decision tree model named Umami_YYDS (89.6% accuracy) was established by data enhancement, comparison algorithm and model optimization. Our model showed a great prediction performance compared to other models, and its outstanding ability was verified by sensory experiments. To provide a convenient approach, we deployed a prediction website based on Umami_YYDS and uploaded the Auto_Taste_ML machine learning package. In summary, we established the system TastePeptides-Meta, containing a taste peptide database TastePeptidesDB an umami/bitter taste prediction model Umami_YYDS and an open-source machine learning package Auto_Taste_ML, which were helpful for rapid screening of umami peptides.

Real-time public transit ridership flow and origin-destination (O–D) information is essential for improving transit service quality and optimizing transit networks in smart cities. The effectiveness and accuracy of the traditional survey-based methods and smart card data-driven methods for O–D information inference have multiple disadvantages in terms of biased results, high latency, insufficient sample size, and the high cost of time and energy. By considering the ubiquity of smart mobile devices in the world, monitoring public transit ridership flow can be accomplished by passively sensing Wi-Fi and Bluetooth (BT) mobile devices of passengers. This study proposed a system for monitoring real-time public transit passenger ridership flow and O–D information based on customized Wi-Fi and BT sensing device. By combining the consideration of the assumed overlapping feature spaces of passenger and nonpassenger media access control address data, a three-step data-driven algorithm framework for estimating transit ridership flow and O–D information is proposed. The observed ridership flow is used as the ground truth for evaluating the performance of the proposed algorithm. According to the evaluation results, the proposed algorithm outperformed all selected baseline models and the existing filtering methods. The findings of this study can help to provide real time and precise transit ridership flow and O–D information for supporting transit vehicle management and the quality of service enhancement.

Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM), to learn the interactions between roadways in the traffic network and forecast the network-wide traffic state. We define the traffic graph convolution based on the physical network topology. The relationship between the proposed traffic graph convolution and the spectral graph convolution is also discussed. An L1-norm on graph convolution weights and an L2-norm on graph convolution features are added to the model’s loss function to enhance the interpretability of the proposed model. Experimental results show that the proposed model outperforms baseline methods on two real-world traffic state datasets. The visualization of the graph convolution weights indicates that the proposed framework can recognize the most influential road segments in real-world traffic networks.

Vitamin D deficiency is a candidate risk factor for osteoporosis, characterized by decreased bone mineral density (BMD). We performed this two-sample Mendelian randomization (MR) analysis to investigate the causal effect of vitamin D on BMD. We extracted 143 single-nucleotide polymorphisms from a recent GWAS on 417,580 participants of European ancestry as instrumental variables, and used summary statistics for BMD at forearm (n = 10,805), femoral neck (n = 49,988), lumbar spine (n = 44,731) and total-body of different age-stages ( 60) (n = 67,358). We explored the direct effect of vitamin D on BMD with an adjusted body mass index (BMI) in a multivariable MR analysis. We found no support for causality of 25-hydroxyvitamin D on BMD at forearm, femoral neck, lumbar spine, and total-body BMD across the lifespan. There was no obvious difference between the total and direct effect of vitamin D on BMD after adjusting for BMI. Our MR analysis provided evidence that genetically determined vitamin D was not causally associated with BMD in the general population. Large-scale randomized controlled trials are warranted to investigate the role of vitamin D supplementation in preventing osteoporosis in the high-risk population.

Cancer is the leading cause of death in China and a significant public health problem with increasing incidence and fatality rates. The Han nationality is the main ethnic group in China, and many reports on the epidemiology of cancers in Han nationality are published. However no studies report the cancer spectrum of Uygurs which are one of the minority nationalities in China. Hence, we present incidence and mortality numbers of different cancers for the Uygur patients for the period 2018-2020 in Hotan District where Uygur population accounts for 99 %. During the 3-year study period, 2509 new Uygur cancer cases were registered, comprising 774 men and 1735 women. Cervical cancer was the most common, followed by esophageal, breast, gastric and colorectal cancers. The most common cancers in women and men were cervical cancer and esophageal cancer, respectively. In conclusion, the cancer spectrum in Hotan is different from other regions of China and our research revealed the cancer incidence in Hotan, which could help us to take appropriate measures to reduce the incidence rate.

With the rapid development of synthetic biology, the oleaginous yeast Yarrowia lipolytica has become an attractive microorganism for chemical production. To better optimize and reroute metabolic pathways, we have expanded the CRISPR-based gene expression toolkit of Y. lipolytica. By sorting the integration sites associated with high expression, new neutral integration sites associated with high expression and high integration efficiency were identified. Diverse genetic components, including promoters and terminators, were also characterized to expand the expression range. We found that in addition to promoters, the newly characterized terminators exhibited large variations in gene expression. These genetic components and integration sites were then used to regulate genes involved in the lycopene biosynthesis pathway, and different levels of lycopene production were achieved. The CRISPR-based gene expression toolkit developed in this study will facilitate the genetic engineering of Y. lipolytica.

Background Serum albumin concentration had been proposed to be an important changeable factor increasing the risk of infection following a procedure, and a perioperative albumin supplementation was recommended in some clinical scenarios. However, the results of the published epidemiological observational studies on these hypotheses are still inconclusive. Here, we performed a two-sample Mendelian randomization (MR) analysis to investigate the causal effect of albumin on infection following a procedure using publically available genome-wide association studies (GWAS) summary statistics. Methods A series of quality control steps were taken in our analysis to select eligible instrumental single nucleotide polymorphisms (SNPs) which were strongly associated with exposure. To make the conclusions more robust and reliable, we utilized several robust analytical methods (inverse-variance weighted, MR-Egger regression, and Weighted median method) that are based on different assumptions of two-sample MR analysis. The Cochran’s Q test, MR- Pleiotropy RESidual Sum and Outlier (PRESSO) global test, MR-Egger intercept test, and “leave-one-out” sensitivity analysis were performed to evaluate the heterogeneity, horizontal pleiotropy, and stability of these genetic variants on outcome. Outlier variants were removed step-by-step to reduce heterogeneity and the effect of horizontal pleiotropy if identified by the MR-PRESSO outlier test. Results Our two-sample MR analysis with one group of exposure GWAS summary statistics and two groups of outcome GWAS summary statistics did not suggest the causal effect of increased/decreased albumin concentration on infection following a procedure. We did not observe the evidence of heterogeneity or directional pleiotropy. There was no potentially influential SNP driving the causal link under “leave-one-out” analysis and the result was stable. Conclusions Our MR analysis did not support the causal effect of albumin on infection following a procedure. The result was robust based on several analytical methods. Perioperative albumin supplementation was questionable and should be investigated with more randomized controlled trials.

Road surface friction significantly impacts traffic safety and mobility. A precise road surface friction prediction model can help to alleviate the influence of inclement road conditions on traffic safety, Level of Service, traffic mobility, fuel efficiency, and sustained economic productivity. Most related previous studies are laboratory-based methods that are difficult for practical implementation. Moreover, in other data-driven methods, the demonstrated time-series features of road surface conditions have not been considered. This study employed a Long-Short Term Memory (LSTM) neural network to develop a data-driven road surface friction prediction model based on historical data. The proposed prediction model outperformed the other baseline models in terms of the lowest value of predictive performance measurements. The influence of the number of time-lags and the predicting time interval on predictive accuracy was analyzed. In addition, the influence of adding road surface water thickness, road surface temperature and air temperature on predictive accuracy also were investigated. The findings of this study can support road maintenance strategy development and decision making, thus mitigating the impact of inclement road conditions on traffic mobility and safety. Future work includes a modified LSTM-based prediction model development by accommodating flexible time intervals between time-lags., Comment: arXiv admin note: text overlap with arXiv:1911.00605

Unmanned aerial vehicle (UAV) is at the heart of modern traffic sensing research due to its advantages of low cost, high flexibility, and wide view range over traditional traffic sensors. Recently, increasing efforts in UAV-based traffic sensing have been made, and great progress has been achieved on the estimation of aggregated macroscopic traffic parameters. Compared to aggregated macroscopic traffic data, there has been extensive attention on higher-resolution traffic data such as microscopic traffic parameters and lane-level macroscopic traffic parameters since they can help deeply understand traffic patterns and individual vehicle behaviours. However, little existing research can automatically estimate microscopic traffic parameters and lane-level macroscopic traffic parameters using UAV videos with a moving background. In this study, an advanced framework is proposed to bridge the gap. Specifically, three functional modules consisting of multiple processing streams and the interconnections among them are carefully designed with the consideration of UAV video features and traffic flow characteristics. Experimental results on real-world UAV video data demonstrate promising performances of the framework in microscopic and lane-level macroscopic traffic parameters estimation. This research pushes off the boundaries of the applicability of UAVs and has an enormous potential to support advanced traffic sensing and management.

An accurate road surface friction forecasting algorithm can allow travelers and managers to schedule trips and maintenance activities based on the road weather condition to enhance traffic safety and efficiency in advance. Previously, scholars developed multiple forecasting models to predict road surface conditions using historical data. However, historical dataset used for model training may have missing values caused by multiple issues, e.g. the data collected by on-vehicle sensors may be influenced when vehicles cannot travel due to high economic and labor cost or weather-related issues.The missing values in the road surface condition dataset can damage the effectiveness and accuracy of the existing prediction methods. This study proposed a road surface friction forecasting algorithm by employing a time-aware Gated Recurrent Unit (GRU-D) networks that integrate a decay mechanism as extra gates of the GRU to handle the missing values and forecast the road surface friction in future periods simultaneously. The evaluation results present that the proposed GRU-D networks outperform all selected baseline algorithms. The impacts of missing rate on predictive accuracy, learning efficiency, and learned decay rates are investigated as well. The findings can help improve the forecasting accuracy and efficiency of road surface friction prediction using historical data with missing values, therefore mitigating the negative impact of wet or icy road conditions on traffic safety and efficiency.

Traffic speed prediction is a critically important component of intelligent transportation systems. Recently, with the rapid development of deep learning and transportation data science, a growing body of new traffic speed prediction models have been designed that achieved high accuracy and large-scale prediction. However, existing studies have two major limitations. First, they predict aggregated traffic speed rather than lane-level traffic speed; second, most studies ignore the impact of other traffic flow parameters in speed prediction. To address these issues, the authors propose a two-stream multi-channel convolutional neural network (TM-CNN) model for multi-lane traffic speed prediction considering traffic volume impact. In this model, the authors first introduce a new data conversion method that converts raw traffic speed data and volume data into spatial–temporal multi-channel matrices. Then the authors carefully design a two-stream deep neural network to effectively learn the features and correlations between individual lanes, in the spatial–temporal dimensions, and between speed and volume. Accordingly, a new loss function that considers the volume impact in speed prediction is developed. A case study using 1-year data validates the TM-CNN model and demonstrates its superiority. This paper contributes to two research areas: (1) traffic speed prediction, and (2) multi-lane traffic flow study.

As the market penetration rate of automated vehicles (AVs) increases, there will be a transition period when the traffic stream is composed of both AVs and human-driven vehicles (MVs) in the near future. However, the interactions between MVs and AVs, especially whether MVs will behave differently when following AVs compared to following MVs, have not been fully understood. Previous studies in this field mainly conducted traffic/numerical simulations or field experiments to investigate human drivers' behavior changes, but these approaches all have critical drawbacks such as simplified driving environments and limited sample sizes. To fill in the knowledge gap, this study uses the high-resolution (10 Hz) Waymo Open Dataset to reveal differences in car-following behaviors between MV-following-AV and MV-following-MV cases. Driving volatility measures, time headways and time-to-collision (TTC) are adopted to quantify and compare MV-following-AV and MV-following-MV interactions. The principal component analysis (PCA) is applied on the high-dimensional feature space, followed by the hierarchical clustering on the dimension-reduced feature set to categorize MV driving styles when following AVs. The comparison results indicate that MV-following-AV events have lower driving volatility in terms of velocity and acceleration/deceleration, smaller time headways and higher TTC values. Furthermore, the clustering results reveal that human drivers when following AVs exhibit four different car-following styles: high-velocity-non-aggressive, high-velocity-aggressive, low-velocity-non-aggressive, and low-velocity-aggressive. These findings highlight the vital importance of taking into account the heterogeneity of MV-following-AV behaviors when designing mixed traffic control algorithms and can be beneficial for AV fleet operators to improve their algorithms.

Until recently, it remains unclear whether schizophrenia, bipolar disorder (BD), and Alzheimer’s disease (AD) is associated with bone mineral density (BMD). We aimed to investigate the causal effects of schizophrenia, BD and AD on BMD with Mendelian randomization (MR) analysis. Single-nucleotide polymorphisms (SNPs) strongly associated with these three neuropsychiatric diseases as instrumental variables were selected from genome-wide association studies in the MR Base database. We analyzed the effects of these SNPs on the femoral neck BMD (FN-BMD), lumbar spine BMD (LS-BMD) and forearm BMD (FA-BMD), and evaluated the heterogeneities and pleiotropy of these genetic variants. We also evaluated the potential confounding factors in the association between these three neuropsychiatric diseases and the BMD level. It was found that none of these genetic variants were significantly associated with BMD or confounding factors. Using these genetic variants, we did not find statistically significant causal effects of per unit increase in the log-odds of having schizophrenia, BD or AD with FN-BMD, LS-BMD and FA-BMD changes (e.g. schizophrenia and FN-BMD, MR-Egger OR 0.9673, 95% CI 0.8382 to 1.1163, p = 0.6519). The MR results also revealed that directional pleiotropy was unlikely to bias the causality (e.g., schizophrenia and FN-BMD, intercept = 0.0023, p = 0.6887), and no evidence of heterogeneity was found between the genetic variants (e.g., schizophrenia and FN-BMD, MR-Egger Q = 46.1502, I2 = 0.0899, p = 0.3047). Our MR study did not support causal effects of increased risk of schizophrenia, BD and AD status with BMD level.

Genome-scale mutagenesis, phenotypic screening, and tracking the causal mutations is a powerful approach for genetic analysis. However, classic mutagenesis approaches require extensive effort to identify causal mutations. It is desirable to demonstrate a powerful approach for rapid trackable mutagenesis. Here, we mapped the distribution of nonhomologous end joining (NHEJ)-mediated integration for the first time and demonstrated that it can be used for constructing the genome-scale trackable mutagenesis library in

Umami, providing amino acids/peptides for animal growth, represents one of the major attractive taste modalities. The biochemical and umami properties of peptide are both important for scientific research and food industry. In this study, we did the sequence analysis of 205 umami peptides with 2-18 amino acids, sought the active sites of umami peptides by quantum chemical simulations and investigated their recognition residues with receptor T1R1/T1R3 by molecular docking. The results showed the peptides with 2-3 amino acids accounting for 44% of the total umami peptides. Residues D and E are the key active sites no matter where they in peptides (N-terminal, C-terminal or middle), when umami peptides contain D/E residues. N69, D147, R151, A170, S172, S276 and R277 residues in T1R1 receptor were deem to the key residues binding umami peptides. Finally, a powerful decision rule for umami peptides was proposed to predict potential umami peptides, which was convenient, time saving and efficiently.