Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools

Big data has been used widely in many areas including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted for improving the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized and the off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies

An Evaluation of Machine Learning and Deep Learning Models for Drought Prediction using Weather Data

Drought is a serious natural disaster that has a long duration and a wide range of influence. To decrease the drought-caused losses, drought prediction is the basis of making the corresponding drought prevention and disaster reduction measures. While this problem has been studied in the literature, it remains unknown whether drought can be precisely predicted or not with machine learning models using weather data. To answer this question, a real-world public dataset is leveraged in this study and different drought levels are predicted using the last 90 days of 18 meteorological indicators as the predictors. In a comprehensive approach, 16 machine learning models and 16 deep learning models are evaluated and compared. The results show no single model can achieve the best performance for all evaluation metrics simultaneously, which indicates the drought prediction problem is still challenging. As benchmarks for further studies, the code and results are publicly available in a Github repository.Comment: Github link: https://github.com/jwwthu/DL4Climate/tree/main/DroughtPredictio

Characterization, Genomic Organization, Abundance, and Chromosomal Distribution of Ty1-copia Retrotransposons in Erianthus arundinaceus

Erianthus arundinaceus is an important wild species of the genus Saccharum with many valuable traits. However, the composition and structure of its genome are largely unknown, which have hindered its utilization in sugarcane breeding and evolutionary research. Retrotransposons constitute an appreciable fraction of plant genomes and may have played a significant role in the evolution and sequence organization of genomes. In the current study, we investigate the phylogenetic diversity and genomic abundance of Ty1-copia retrotransposons for the first time and inspect their chromosomal distribution patterns in E. arundinaceus. In total, 70 Ty1-copia reverse transcriptase (RT) sequences with significant levels of heterogeneity were obtained. The phylogenetic analysis revealed these Ty1-copia retrotransposons were classified into four distinct evolutionary lineages (Tork/TAR, Tork/Angela, Retrofit/Ale, and Sire/Maximus). Dot-blot analysis showed estimated the total copy number of Ty1-copia retrotransposons to be about 4.5 × 103 in the E. arundinaceus genome, indicating they were a significant component. Fluorescence in situ hybridization revealed that Ty1-copia retrotransposons from the four lineages had strikingly similar patterns of chromosomal enrichment, being exclusively enriched in the subterminal heterochromatic regions of most E. arundinaceus chromosomes. This is the first clear evidence of the presence of Ty1-copia retrotransposons in the subterminal heterochromatin of E. arundinaceus. Altogether, these results promote the understanding of the diversification of Ty1-copia retrotransposons and shed light on their chromosomal distribution patterns in E. arundinaceus

The fermi arc and fermi pocket in cuprates in a short-range diagonal stripe phase

In this paper we studied the fermi arc and the fermi pocket in cuprates in a short-range diagonal stripe phase with wave vectors $(7\pi/8, 7\pi/8)$, which reproduce with a high accuracy the positions and sizes of the fermi arc and fermi pocket and the superstructure in cuprates observed by Meng et al\cite{Meng}. The low-energy spectral function indicates that the fermi pocket results from the main band and the shadow band at the fermi energy. Above the fermi energy the shadow band gradually departs away from the main band, leaving a fermi arc. Thus we conclude that the fermi arc and fermi pocket can be fully attributed to the stripe phase but has nothing to do with pairing. Incorporating a d-wave pairing potential in the stripe phase the spectral weight in the antinodal region is removed, leaving a clean fermi pocket in the nodal region.Comment: 5 pages, 6 figure

Control of Cyclin D1 and Breast Tumorigenesis by the EglN2 Prolyl Hydroxylase

Summary2-Oxoglutarate-dependent dioxygenases, including the EglN prolyl hydroxylases that regulate HIF, can be inhibited with drug-like molecules. EglN2 is estrogen inducible in breast carcinoma cells and the lone Drosophila EglN interacts genetically with Cyclin D1. Although EglN2 is a nonessential gene, we found that EglN2 inactivation decreases Cyclin D1 levels and suppresses mammary gland proliferation in vivo. Regulation of Cyclin D1 is a specific attribute of EglN2 among the EglN proteins and is HIF independent. Loss of EglN2 catalytic activity inhibits estrogen-dependent breast cancer tumorigenesis and can be rescued by exogenous Cyclin D1. EglN2 depletion also impairs the fitness of lung, brain, and hematopoietic cancer lines. These findings support the exploration of EglN2 inhibitors as therapeutics for estrogen-dependent breast cancer and other malignancies

Stem Cell Factor SALL4 Represses the Transcriptions of PTEN and SALL1 through an Epigenetic Repressor Complex

Background The embryonic stem cell (ESC) factor, SALL4, plays an essential role in both development and leukemogenesis. It is a unique gene that is involved in self-renewal in ESC and leukemic stem cell (LSC).Methodology/Principal Findings To understand the mechanism(s) of SALL4 function(s), we sought to identify SALL4-associated proteins by tandem mass spectrometry. Components of a transcription repressor Mi-2/Nucleosome Remodeling and Deacetylase (NuRD) complex were found in the SALL4-immunocomplexes with histone deacetylase (HDAC) activity in ESCs with endogenous SALL4 expression and 293T cells overexpressing SALL4. The SALL4-mediated transcriptional regulation was tested on two potential target genes: PTEN and SALL1. Both genes were confirmed as SALL4 downstream targets by chromatin-immunoprecipitation, and their expression levels, when tested by quantitative reverse transcription polymerase chain reaction (qRT-PCR), were decreased in 293T cells overexpressing SALL4. Moreover, SALL4 binding sites at the promoter regions of PTEN and SALL1 were co-occupied by NuRD components, suggesting that SALL4 represses the transcriptions of PTEN and SALL1 through its interactions with the Mi-2/NuRD complex. The in vivo repressive effect(s) of SALL4 were evaluated in SALL4 transgenic mice, where decreased expressions of PTEN and SALL1 were associated with myeloid leukemia and cystic kidneys, respectively.Conclusions/Significance In summary, we are the first to demonstrate that stem cell protein SALL4 represses its target genes, PTEN and SALL1, through the epigenetic repressor Mi-2/NuRD complex. Our novel finding provides insight into the mechanism(s) of SALL4 functions in kidney development and leukemogenesis

Experimental and Numerical Analysis and Prediction of Ground Vibrations Due to Heavy Haul Railway Viaduct

The ground vibration wave induced by the viaduct section of the heavy freight wagons is transformed into the ground vibration problem under the condition of point source excitation after it is transmitted to soil through pier. When the speed and axle load of wagon become larger and larger, the impact on the surrounding buildings will also increase. In this paper, the ground vibration around Shenshan Village in section of Shuo-Huang railway line is monitored and numerical analyzed; the 3D numerical model of the Bridge-Pier-Field-House system is established. The relationship between peak vibration velocity (PPV) and the distance to the pier caused by heavy freight wagons at different speeds and different type of wagons is analyzed. The power function relationship between the two when measuring line perpendicular to the centerline of railway is verified. Based on the modified Sodev’s equation, the relationship between PPV of ground points and wagon speed, axle load, and soil properties is proposed, and the value of every parameter in the formula is discussed in detail. The concept of energy index is put forward for the first time in the formula, and the relationship between energy index and wagon speed and wagon weight is analyzed by regression analysis. The accuracy of the calculation model and prediction formula is verified by comparing field test results; an analytical method is proposed to predict the ground vibration induced by viaduct

Graph neural network for traffic forecasting: the research progress

Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning, road traffic control, and vehicle routing. Various forecasting methods have been proposed in the literature, including statistical models, shallow machine learning models, and deep learning models. Recently, graph neural networks (GNNs) have emerged as state-of-the-art traffic forecasting solutions because they are well suited for traffic systems with graph structures. This survey aims to introduce the research progress on graph neural networks for traffic forecasting and the research trends observed from the most recent studies. Furthermore, this survey summarizes the latest open-source datasets and code resources for sharing with the research community. Finally, research challenges and opportunities are proposed to inspire follow-up research.Published versionThis research was supported by the Fundamental Research Funds for the Central Universities