AccidentGPT: Large Multi-Modal Foundation Model for Traffic Accident Analysis

Traffic accident analysis is pivotal for enhancing public safety and developing road regulations. Traditional approaches, although widely used, are often constrained by manual analysis processes, subjective decisions, uni-modal outputs, as well as privacy issues related to sensitive data. This paper introduces the idea of AccidentGPT, a foundation model of traffic accident analysis, which incorporates multi-modal input data to automatically reconstruct the accident process video with dynamics details, and furthermore provide multi-task analysis with multi-modal outputs. The design of the AccidentGPT is empowered with a multi-modality prompt with feedback for task-oriented adaptability, a hybrid training schema to leverage labelled and unlabelled data, and a edge-cloud split configuration for data privacy. To fully realize the functionalities of this model, we proposes several research opportunities. This paper serves as the stepping stone to fill the gaps in traditional approaches of traffic accident analysis and attract the research community attention for automatic, objective, and privacy-preserving traffic accident analysis.Comment: 8 pages, 2 figure

Contagious bank runs and committed liquidity support

In a crisis, regulators and private investors can find it difficult, if not impossible, to tell whether banks facing runs are insolvent or merely illiquid. We introduce such an information constraint into a global-games-based bank run model with multiple banks and aggregate uncertainties. The information constraint creates a vicious cycle between contagious bank runs and falling asset prices and limits the effectiveness of traditional emergency liquidity assistance programs. We explain how a regulator can set up committed liquidity support to contain contagion and stabilize asset prices even without information on banks’ solvency, rationalizing some recent developments in policy practices

Variations of China's emission estimates:Response to uncertainties in energy statistics

The accuracy of China's energy statistics is of great concern because it contributes greatly to the uncertainties in estimates of global emissions. This study attempts to improve the understanding of uncertainties in China's energy statistics and evaluate their impacts on China's emissions during the period of 1990-2013. We employed the Multi-resolution Emission Inventory for China (MEIC) model to calculate China's emissions based on different official data sets of energy statistics using the same emission factors. We found that the apparent uncertainties (maximum discrepancy) in China's energy consumption increased from 2004 to 2012, reaching a maximum of 646Mtce (million tons of coal equivalent) in 2011 and that coal dominated these uncertainties. The discrepancies between the national and provincial energy statistics were reduced after the three economic censuses conducted during this period, and converging uncertainties were found in 2013. The emissions calculated from the provincial energy statistics are generally higher than those calculated from the national energy statistics, and the apparent uncertainty ratio (the ratio of the maximum discrepancy to the mean value) owing to energy uncertainties in 2012 took values of 30.0, 16.4, 7.7, 9.2 and 15.6%, for SO2, NOx, VOC, PM2.5 and CO2 emissions, respectively. SO2 emissions are most sensitive to energy uncertainties because of the high contributions from industrial coal combustion. The calculated emission trends are also greatly affected by energy uncertainties - from 1996 to 2012, CO2 and NOx emissions, respectively, increased by 191 and 197% according to the provincial energy statistics but by only 145 and 139% as determined from the original national energy statistics. The energy-induced emission uncertainties for some species such as SO2 and NOx are comparable to total uncertainties of emissions as estimated by previous studies, indicating variations in energy consumption could be an important source of China's emission uncertainties

Characteristics and sources of water-soluble organic aerosol in a heavily polluted environment in Northern China

Water-soluble organic aerosol (WSOA) in fine particles (PM2.5) collected during wintertime in a polluted city (Handan) in Northern China was characterized using a High-Resolution Time-of-Flight Aerosol Mass Spectrometer (AMS). Through comparing with real-time measurements from a collocated Aerosol Chemical Speciation Monitor (ACSM), we determined that WSOA on average accounts for 29% of total organic aerosol (OA) mass and correlates tightly with secondary organic aerosol (SOA; Pearson's r = 0.95). The mass spectra of WSOA closely resemble those of ambient SOA, but also show obvious influences from coal combustion and biomass burning. Positive matrix factorization (PMF) analysis of the WSOA mass spectra resolved a water-soluble coal combustion OA (WS-CCOA; O/C = 0.17), a water-soluble biomass burning OA (WS-BBOA; O/C = 0.32), and a water-soluble oxygenated OA (WS-OOA; O/C = 0.89), which account for 10.3%, 29.3% and 60.4% of the total WSOA mass, respectively. The water-solubility of the OA factors was estimated by comparing the offline AMS analysis results with the ambient ACSM measurements. OOA has the highest water-solubility of 49%, consistent with increased hygroscopicity of oxidized organics induced by atmospheric aging processes. In contrast, CCOA is the leastwater soluble, containing 17% WS-CCOA. The distinct characteristics of WSOA from different sources extend our knowledge of the complex aerosol chemistry in the polluted atmosphere of Northern China and the water-solubility analysis may help us to understand better aerosol hygroscopicity and its effects on radiative forcing in this region. (C) 2020 Published by Elsevier B.V.Peer reviewe

Data Mining in Networks of Differentially Expressed Genes during Sow Pregnancy

Small to moderate gains in Pig fertility can mean large returns in overall efficiency, and developing methods to improve it is highly desirable. High fertility rates depend on completion of successful pregnancies. To understand the molecular signals associated with pregnancy in sows, expression profiling experiments were conducted to identify differentially expressed genes in ovary and myometrium at different pregnancy periods using the Affymetrix Porcine GeneChipTM. A total of 974, 1800, 335 and 710 differentially expressed transcripts were identified in the myometrium during early pregnancy (EP) and late pregnancy (LP), and in the ovary during EP and LP, respectively. Self-Organizing Map (SOM) clusters indicated the differentially expressed genes belonged to 7 different functional groups. Based on BLASTX searches and Gene Ontology (GO) classifications, 129 unique genes closely related to pregnancy showed differential expression patterns. GO analysis also indicated that there were 21 different molecular function categories, 20 different biological process categories, and 8 different cellular component categories of genes differentially expressed during sow pregnancy. Gene regulatory network reconstruction provided us with an interaction model of known genes such as insulin-like growth factor 2 (IGF2) gene, estrogen receptor (ESR) gene, retinol-binding protein-4 (RBP4) gene, and several unknown candidate genes related to reproduction. Several pitch point genes were selected for association study with reproduction traits. For instance, DPPA5 g.363 T>C was found to associate with litter born weight at later parities in Beijing Black pigs significantly (p < 0.05). Overall, this study contributes to elucidating the mechanism underlying pregnancy processes, which maybe provide valuable information for pig reproduction improvement

Genome-wide identification and expression analysis of the MYB transcription factor in moso bamboo (Phyllostachys edulis)

The MYB family, one of the largest transcription factor (TF) families in the plant kingdom, plays vital roles in cell formation, morphogenesis and signal transduction, as well as responses to biotic and abiotic stresses. However, the underlying function of bamboo MYB TFs remains unclear. To gain insight into the status of these proteins, a total of 85 PeMYBs, which were further divided into 11 subgroups, were identified in moso bamboo (Phyllostachys edulis) by using a genome-wide search strategy. Gene structure analysis showed that PeMYBs were significantly different, with exon numbers varying from 4 to 13. Phylogenetic analysis indicated that PeMYBs clustered into 27 clades, of which the function of 18 clades has been predicted. In addition, almost all of the PeMYBs were differently expressed in leaves, panicles, rhizomes and shoots based on RNA-seq data. Furthermore, qRT-PCR analysis showed that 12 PeMYBs related to the biosynthesis and deposition of the secondary cell wall (SCW) were constitutively expressed, and their transcript abundance levels have changed significantly with increasing height of the bamboo shoots, for which the degree of lignification continuously increased. This result indicated that these PeMYBs might play fundamental roles in SCW thickening and bamboo shoot lignification. The present comprehensive and systematic study on the members of the MYB family provided a reference and solid foundation for further functional analysis of MYB TFs in moso bamboo

Genome-wide Association Study of Porcine Hematological Parameters in a Large White × Minzhu F2 Resource Population

Hematological traits, which are important indicators of immune function in animals, have been commonly examined as biomarkers of disease and disease severity in humans and animals. Genome-wide significant quantitative trait loci (QTLs) provide important information for use in breeding programs of animals such as pigs. QTLs for hematological parameters (hematological traits) have been detected in pig chromosomes, although these are often mapped by linkage analysis to large intervals making identification of the underlying mutation problematic. Single nucleotide polymorphisms (SNPs) are the common form of genetic variation among individuals and are thought to account for the majority of inherited traits. In this study, a genome-wide association study (GWAS) was performed to detect regions of association with hematological traits in a three-generation resource population produced by intercrossing Large White boars and Minzhu sows during the period from 2007 to 2011. Illumina PorcineSNP60 BeadChip technology was used to genotype each animal and seven hematological parameters were measured (hematocrit (HCT), hemoglobin (HGB), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV), red blood cell count (RBC) and red blood cell volume distribution width (RDW)). Data were analyzed in a three step Genome-wide Rapid Association using the Mixed Model and Regression-Genomic Control (GRAMMAR-GC) method. A total of 62 genome-wide significant and three chromosome-wide significant SNPs associated with hematological parameters were detected in this GWAS. Seven and five SNPs were associated with HCT and HGB, respectively. These SNPs were all located within the region of 34.6-36.5 Mb on SSC7. Four SNPs within the region of 43.7-47.0 Mb and fifty-five SNPs within the region of 42.2-73.8 Mb on SSC8 showed significant association with MCH and MCV, respectively. At chromosome-wide significant level, one SNP at 29.2 Mb on SSC1 and two SNPs within the region of 26.0-26.2 Mb were found to be significantly associated with RBC and RDW, respectively. Many of the SNPs were located within previously reported QTL regions and appeared to narrow down the regions compared with previously described QTL intervals. In current research, a total of seven significant SNPs were found within six candidate genes SCUBE3, KDR, TDO, IGFBP7, ADAMTS3 and AFP. In addition, the KIT gene, which has been previously reported to relate to hematological parameters, was located within the region significantly associated with MCH and MCV and could be a candidate gene. These results of this study may lead to a better understanding of the molecular mechanisms of hematological parameters in pigs