Predicting Automobile Accident Severity and Hotspots Using Multinomial Logistic Regression

Title: Predicting automobile accident severity and hotspots using multinomial logistic regression. Americans are now driving more than ever [1]. In 2010, close to 33,000 lives were lost and another estimated 3.9 million people were injured in automobile accidents; all things considered, these accidents accounted for $836 billion in damages [2]. Since then, the rate of automobile-related deaths per 100 million miles traveled has not shown signs of improvement [3]. This research expands upon a previous year’s poster presented at the South Dakota State University Data Science Symposium 2019 [4]. While the previous research focuses on a data visualization of automobile accident hotspots on a map based on the severity and frequency of accidents, this research aims to train a multinomial logistic regression machine learning model using data related to weather conditions, speed limit, and GPS coordinates to predict the severity of automobile accidents. The development of such a machine learning model can help inform emergency services better manage resources in anticipation of potential automobile accidents based on prevailing weather conditions, speed limit along a stretch of road, and location data. An updated version of the previous dataset will be used. This dataset contains approximately 1.5 million automobile accident data points, collected over a span of over four years, from February 2016 to December 2020. References [1] US Department of Transportation. Federal Highway Administration. (May, 2019). Strong economy has Americans driving more than ever before. Retrieved from https://cms8.fhwa.dot.gov/newsroom/strong-economy-has-americans-driving-more-ever [2] Blincoe, L. J., Miller, T. R., Zaloshnja, E., & Lawrence, B. A. (2015, May). The economic and societal impact of motor vehicle crashes, 2010. (Revised)(Report No. DOT HS 812 013). Washington, DC: National Highway Traffic Safety Administration. [3] National Center for Statistics and Analysis (2019, December). Early estimate of motor vehicle traffic fatalities for the first 9 months (Jan – Sep) of 2019. (Crash Stats Brief Statistical Summary. Report No. DOT HS 812 874). Washington, DC: Highway Traffic Safety Administration. [4] Identification of Automobile Accident Hotspots using Countrywide Traffic Accident Dataset, B. Z. Yang & S. Z. Sajal, Ph.D., Presented at 2020 South Dakota State University Data Science Symposium

Acetylation modification regulates GRP78 secretion in colon cancer cells

High glucose-regulated protein 78 (GRP78) expression contributes to the acquisition of a wide range of phenotypic cancer hallmarks, and the pleiotropic oncogenic functions of GRP78 may result from its diverse subcellular distribution. Interestingly, GRP78 has been reported to be secreted from solid tumour cells, participating in cell-cell communication in the tumour microenvironment. However, the mechanism underlying this secretion remains elusive. Here, we report that GRP78 is secreted from colon cancer cells via exosomes. Histone deacetylase (HDAC) inhibitors blocked GRP78 release by inducing its aggregation in the ER. Mechanistically, HDAC inhibitor treatment suppressed HDAC6 activity and led to increased GRP78 acetylation; acetylated GRP78 then bound to VPS34, a class III phosphoinositide-3 kinase, consequently preventing the sorting of GRP78 into multivesicular bodies (MVBs). Of note, we found that mimicking GRP78 acetylation by substituting the lysine at residue 633, one of the deacetylated sites of HDAC6, with a glutamine resulted in decreased GRP78 secretion and impaired tumour cell growth in vitro. Our study thus reveals a hitherto-unknown mechanism of GRP78 secretion and may also provide implications for the therapeutic use of HDAC inhibitors

A novel defined risk signature of cuproptosis-related long non-coding RNA for predicting prognosis, immune infiltration, and immunotherapy response in lung adenocarcinoma

Background: Cuproptosis is a newly discovered non-apoptotic form of cell death that may be related to the development of tumors. Nonetheless, the potential role of cuproptosis-related lncRNAs in tumor immunity formation and patient-tailored treatment optimization of lung adenocarcinoma (LUAD) is still unclear.Methods: RNA sequencing and survival data of LUAD patients were downloaded from The Cancer Genome Atlas (TCGA) database for model training. The patients with LUAD in GSE29013, GSE30219, GSE31210, GSE37745, and GSE50081 were used for validation. The proofed cuproptosis-related genes were extracted from the previous studies. The Pearson correlation was applied to select cuproptosis-related lncRNAs. We chose differentially expressed cuproptosis-related lncRNAs in the tumor and normal tissues and allowed them to go to a Cox regression and a LASSO regression for a lncRNA signature that predicts the LUAD prognosis. Kaplan–Meier estimator, Cox model, ROC, tAUC, PCA, nomogram predictor, decision curve analysis, and real-time PCR were further deployed to confirm the model’s accuracy. We examined this model’s link to other regulated cell death forms. Applying TMB, immune-related signatures, and TIDE demonstrated the immunotherapeutic capabilities of signatures. We evaluated the relationship of our signature to anticancer drug sensitivity. GSEA, immune infiltration analysis, and function experiments further investigated the functional mechanisms of the signature and the role of immune cells in the prognostic power of the signature.Results: An eight-lncRNA signature (TSPOAP1-AS1, AC107464.3, AC006449.7, LINC00324, COLCA1, HAGLR, MIR4435-2HG, and NKILA) was built and demonstrated owning prognostic power by applied to the validation cohort. Each signature gene was confirmed differentially expressed in the real world by real-time PCR. The eight-lncRNA signature correlated with 2321/3681 (63.05%) apoptosis-related genes, 11/20 (55.00%) necroptosis-related genes, 34/50 (68.00%) pyroptosis-related genes, and 222/380 (58.42%) ferroptosis-related genes. Immunotherapy analysis suggested that our signature may have utility in predicting immunotherapy efficacy in patients with LUAD. Mast cells were identified as key players that support the predicting capacity of the eight-lncRNA signature through the immune infiltrating analysis.Conclusion: In this study, an eight-lncRNA signature linked to cuproptosis was identified, which may improve LUAD management strategies. This signature may possess the ability to predict the effect of LUAD immunotherapy. In addition, infiltrating mast cells may affect the signature’s prognostic power

Possible Luttinger liquid behavior of edge transport in monolayer transition metal dichalcogenide crystals.

In atomically-thin two-dimensional (2D) semiconductors, the nonuniformity in current flow due to its edge states may alter and even dictate the charge transport properties of the entire device. However, the influence of the edge states on electrical transport in 2D materials has not been sufficiently explored to date. Here, we systematically quantify the edge state contribution to electrical transport in monolayer MoS2/WSe2 field-effect transistors, revealing that the charge transport at low temperature is dominated by the edge conduction with the nonlinear behavior. The metallic edge states are revealed by scanning probe microscopy, scanning Kelvin probe force microscopy and first-principle calculations. Further analyses demonstrate that the edge-state dominated nonlinear transport shows a universal power-law scaling relationship with both temperature and bias voltage, which can be well explained by the 1D Luttinger liquid theory. These findings demonstrate the Luttinger liquid behavior in 2D materials and offer important insights into designing 2D electronics

Session 13: \u3cem\u3ePredicting Automobile Accident Severity and Hotspots Using Multinomial Logistic regression\u3c/em\u3e

Americans are now driving more than ever [1]. In 2010, close to 33,000 lives were lost and another estimated 3.9 million people were injured in automobile accidents; all things considered, these accidents accounted for $836 billion in damages [2]. Since then, the rate of automobile-related deaths per 100 million miles traveled has not shown signs of improvement [3]. This research expands upon a previous year’s poster presented at the South Dakota State University Data Science Symposium 2019 [4]. While the previous research focuses on a data visualization of automobile accident hotspots on a map based on the severity and frequency of accidents, this research aims to train a multinomial logistic regression machine learning model using data related to weather conditions, speed limit, and GPS coordinates to predict the severity of automobile accidents. The development of such a machine learning model can help inform emergency services better manage resources in anticipation of potential automobile accidents based on prevailing weather conditions, speed limit along a stretch of road, and location data. An updated version of the previous dataset will be used. This dataset contains approximately 1.5 million automobile accident data points, collected over a span of over four years, from February 2016 to December 2020

Predicting Severity and Frequency of Automobile Accidents, and Identification of Accident Hotspots in the US

Americans are now driving more than ever (US Department of Transportation, 2019). In 2010, close to 33,000 lives were lost and another estimated 3.9 million people were injured in automobile accidents; all things considered, these accidents accounted for $836 billion in damages. Since then, the rate of automobile-related deaths per 100 million miles traveled has not shown signs of improvement (National Center for Statistics and Analysis, 2019). This research aims to conduct an exploratory data analysis on a dataset containing 2.25 million automobile accident records collected over a span of three years from February 2016 to March 2019, to help predict the severity and frequency of traffic accidents, as well as to identify potential accident “hotspots” across the US

Influence of Processing Parameters on the Mechanical Properties of Peek Plates by Hot Compression Molding

Thermoplastic components are gaining more and more attention due to their advantages which include high specific strength, high toughness, and low manufacturing costs. Despite the fast development of such materials in engineering applications, the major challenge for the wider use of thermoplastic components is the diverse mechanical properties that are caused by uncertain factors during the molding process. In this paper, the effects of processing parameters on the mechanical properties of PEEK plates by hot compression molding are systematically investigated, including the temperature, pressure, and compression time. It was found that both temperature and time can sensitively change the mechanical properties; however, a pressure larger than 1.5 MPa showed a limited impact on the mechanical behaviors of PEEK plates. The optimal process parameters include a hot compression temperature of 400 °C, a compression time of 30 min, and a pressure of 2.5 MPa

Effect of cavity parameters on the propagation of shock wave generated by underwater pulsed discharge

In order to optimize the shock wave generated by underwater pulsed discharge, the relationship between cavity parameters and shock wave propagation is further studied by three-dimensional numerical simulation. According to the sound pressure field distribution obtained by the simulation, the reflection of the shock wave by the reactor wall can be clearly observed. The reflected pressure wave will reach its maximum value and then gradually attenuate. The study also found that when the deposition energy is constant, when the initial radius of the arc channel increases from 0.1 mm to 2.5 mm, the maximum amplitude of the shock wave will increase from 0.22 × 105 Pa to 1.70 × 105 Pa. When the initial radius of the arc channel is constant, as the deposition energy increases, the time to radiate the shock wave becomes earlier, and the maximum amplitude of the shock wave will increase. This means that a higher pressure can be generated by increasing the input of the deposition energy. When the deposition energy is constant, a higher-pressure level can be obtained by increasing the initial radius of the channel. The excitation frequency also affects the shock wave amplitude. Higher excitation frequency can obtain higher pressure amplitude. These methods will increase the efficiency of underwater pulse discharge treatment of bacteria

Peonidin-3-O-Glucoside from Purple Corncob Ameliorates Nonalcoholic Fatty Liver Disease by Regulating Mitochondrial and Lysosome Functions to Reduce Oxidative Stress and Inflammation

A frequent chronic liver condition across the world is nonalcoholic fatty liver disease (NAFLD). Oxidative stress caused by lipid accumulation is generally considered to be the main cause of NAFLD. Anthocyanins can effectively inhibit the production of reactive oxygen species and improve oxidative stress. In this work, six major anthocyanins were separated from purple corncob by semi-preparative liquid chromatography. The effects of the 6 kinds of anthocyanins against NAFLD were investigated using a free fatty acid (FFA)-induced cell model. The results showed that peonidin 3-O-glucoside (P3G) can significantly reduce lipid accumulation in the NAFLD cell model. The treatment with P3G also inhibited oxidative stress via inhibiting the excessive production of reactive oxygen species and superoxide anion, increasing glutathione levels, and enhancing the activities of SOD, GPX, and CAT. Further studies unveiled that treatment with P3G not only alleviated inflammation but also improved the depletion of mitochondrial content and damage of the mitochondrial electron transfer chain developed concomitantly in the cell model. P3G upregulated transcription factor EB (TFEB)-mediated lysosomal function and activated the peroxisome proliferator-activated receptor alpha (PPARα)-mediated peroxisomal lipid oxidation by interacting with PPARα possibly. Overall, this study added to our understanding of the protective effects of purple corn anthocyanins against NAFLD and offered suggestions for developing functional foods containing these anthocyanins