DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting

Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to lack of mining road topology. To address the effect attenuation problem, we propose to take account of the traffic of surrounding locations(wider than adjacent range). We propose an end-to-end framework called DeepTransport, in which Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain spatial-temporal traffic information within a transport network topology. In addition, attention mechanism is introduced to align spatial and temporal information. Moreover, we constructed and released a real-world large traffic condition dataset with 5-minute resolution. Our experiments on this dataset demonstrate our method captures the complex relationship in temporal and spatial domain. It significantly outperforms traditional statistical methods and a state-of-the-art deep learning method

Experimental Study of an Iron-Based Metal-Organic Framework as Flame Retardant for Poly (methyl methacrylate) (PMMA)

PresentationPoly (methyl methacrylate) (PMMA) is a kind of widely used thermoplastic in the family of poly (acrylic ester)s due to its good mechanical properties, like good moldability, high resistance to UV light and weathering, high strength, and excellent dimensional stability. However, PMMA is also characterized by limited heat resistance, poor thermal stability, and high flammability. Metal-organic frameworks (MOFs) are a new class of porous materials, which possess unique physicochemical properties and have attracted considerable interests from different fields, such as energy, gas storage and separation, and catalysis. Additionally, because of their inorganic−organic hybrid nature, MOFs are usually compatible with polymers to form composites. PCN-250 is an iron-based MOF with nitrogen-containing structure and it is chemically stable and physically robust. So far, it can be economically synthesized in large scale. In this study, PCN-250 is used as a potential flame retardant for PMMA. To evaluate the performance of PCN-250 with different concentrations, the thermostability and flame retardancy of the PMMA composites are systematically investigated using thermal gravimetric analysis (TGA) and cone calorimetry. This study will give us some insight about the application of MOFs as a new kind of flame retardant to enhance and improve the fire safety of polymer materials

Equilibrium search with heterogeneous firms, workers and endogenous human capital

This article shows how the endogenous human capital affects the labor market equilibrium when jobs provided by firms can be either unskilled or skilled and workers differ in their education level which can be either low-educated or high-educated. We develop an equilibrium search model in which the high-educated workers are assumed to be able to accept either the unskilled jobs or the skilled jobs, while the low-educated workers can only accept the unskilled jobs. The market equilibrium is characterized by deriving the unemployment rate and the human capital distributions when the growth rate of the human capital is an endogenous variable. The results demonstrate that the structure proportion of the offered jobs affects the equilibrium which shows there is a threshold that can distinguish whether the equilibrium is separating or cross-skill. In addition, the cross-skill equilibrium solution implies the high-educated workers are more likely to own higher pay rates than the low-educated workers with same tenure. It also yields a new insight on the effect of the structure proportion of workers on the profits, which implies the profits of the firms decrease with the increasing number of the low-educated workers. Moreover, the profit of the firms offering the skilled jobs is greater than those offering the unskilled jobs until there is only very few high-educated workers

Cost-minimization predictive energy management of a postal-delivery fuel cell electric vehicle with intelligent battery State-of-Charge Planner

Fuel cell electric vehicles have earned substantial attentions in recent decades due to their high-efficiency and zero-emission features, while the high operating costs remain the major barrier towards their large-scale commercialization. In such context, this paper aims to devise an energy management strategy for an urban postal-delivery fuel cell electric vehicle for operating cost mitigation. First, a data-driven dual-loop spatial-domain battery state-of-charge reference estimator is designed to guide battery energy depletion, which is trained by real-world driving data collected in postal delivery missions. Then, a fuzzy C-means clustering enhanced Markov speed predictor is constructed to project the upcoming velocity. Lastly, combining the state-of-charge reference and the forecasted speed, a model predictive control-based cost-optimization energy management strategy is established to mitigate vehicle operating costs imposed by energy consumption and power-source degradations. Validation results have shown that 1) the proposed strategy could mitigate the operating cost by 4.43% and 7.30% in average versus benchmark strategies, denoting its superiority in term of cost-reduction and 2) the computation burden per step of the proposed strategy is averaged at 0.123ms, less than the sampling time interval 1s, proving its potential of real-time applications

Equilibrium search with heterogeneous firms, workers and endogenous human capital

This article shows how the endogenous human capital affects the labor market equilibrium when jobs provided by firms can be either unskilled or skilled and workers differ in their education level which can be either low-educated or high-educated. We develop an equilibrium search model in which the high-educated workers are assumed to be able to accept either the unskilled jobs or the skilled jobs, while the low-educated workers can only accept the unskilled jobs. The market equilibrium is characterized by deriving the unemployment rate and the human capital distributions when the growth rate of the human capital is an endogenous variable. The results demonstrate that the structure proportion of the offered jobs affects the equilibrium which shows there is a threshold that can distinguish whether the equilibrium is separating or cross-skill. In addition, the cross-skill equilibrium solution implies the high-educated workers are more likely to own higher pay rates than the low-educated workers with same tenure. It also yields a new insight on the effect of the structure proportion of workers on the profits, which implies the profits of the firms decrease with the increasing number of the low-educated workers. Moreover, the profit of the firms offering the skilled jobs is greater than those offering the unskilled jobs until there is only very few high-educated workers

Engineering Properties of Sweet Potato Starch for Industrial Applications by Biotechnological Techniques Including Genome Editing

Sweet potato (Ipomoea batatas) is one of the largest food crops in the world. Due to its abundance of starch, sweet potato is a valuable ingredient in food derivatives, dietary supplements, and industrial raw materials. In addition, due to its ability to adapt to a wide range of harsh climate and soil conditions, sweet potato is a crop that copes well with the environmental stresses caused by climate change. However, due to the complexity of the sweet potato genome and the long breeding cycle, our ability to modify sweet potato starch is limited. In this review, we cover the recent development in sweet potato breeding, understanding of starch properties, and the progress in sweet potato genomics. We describe the applicational values of sweet potato starch in food, industrial products, and biofuel, in addition to the effects of starch properties in different industrial applications. We also explore the possibility of manipulating starch properties through biotechnological means, such as the CRISPR/Cas-based genome editing. The ability to target the genome with precision provides new opportunities for reducing breeding time, increasing yield, and optimizing the starch properties of sweet potatoes

Semi-supervised Optimal Transport with Self-paced Ensemble for Cross-hospital Sepsis Early Detection

The utilization of computer technology to solve problems in medical scenarios has attracted considerable attention in recent years, which still has great potential and space for exploration. Among them, machine learning has been widely used in the prediction, diagnosis and even treatment of Sepsis. However, state-of-the-art methods require large amounts of labeled medical data for supervised learning. In real-world applications, the lack of labeled data will cause enormous obstacles if one hospital wants to deploy a new Sepsis detection system. Different from the supervised learning setting, we need to use known information (e.g., from another hospital with rich labeled data) to help build a model with acceptable performance, i.e., transfer learning. In this paper, we propose a semi-supervised optimal transport with self-paced ensemble framework for Sepsis early detection, called SPSSOT, to transfer knowledge from the other that has rich labeled data. In SPSSOT, we first extract the same clinical indicators from the source domain (e.g., hospital with rich labeled data) and the target domain (e.g., hospital with little labeled data), then we combine the semi-supervised domain adaptation based on optimal transport theory with self-paced under-sampling to avoid a negative transfer possibly caused by covariate shift and class imbalance. On the whole, SPSSOT is an end-to-end transfer learning method for Sepsis early detection which can automatically select suitable samples from two domains respectively according to the number of iterations and align feature space of two domains. Extensive experiments on two open clinical datasets demonstrate that comparing with other methods, our proposed SPSSOT, can significantly improve the AUC values with only 1% labeled data in the target domain in two transfer learning scenarios, MIMIC $rightarrow$ Challenge and Challenge $rightarrow$ MIMIC.Comment: 14 pages, 9 figure

Detection of cerebral tauopathy in P301L mice using high-resolution large-field multifocal illumination fluorescence microscopy

Current intravital microscopy techniques visualize tauopathy with high-resolution, but have a small field-of-view and depth-of-focus. Herein, we report a transcranial detection of tauopathy over the entire cortex of P301L tauopathy mice using large-field multifocal illumination (LMI) fluorescence microscopy technique and luminescent conjugated oligothiophenes. In vitro assays revealed that fluorescent ligand h-FTAA is optimal for in vivo tau imaging, which was confirmed by observing elevated probe retention in the cortex of P301L mice compared to non-transgenic littermates. Immunohistochemical staining further verified the specificity of h-FTAA to detect tauopathy in P301L mice. The new imaging platform can be leveraged in pre-clinical mechanistic studies of tau spreading and clearance as well as longitudinal monitoring of tau targeting therapeutics

Combined live oral priming and intramuscular boosting regimen with Rotarix® and a nanoparticle-based trivalent rotavirus vaccine evaluated in gnotobiotic pig models of G4P[6] and G1P[8] human rotavirus infection

Human rotavirus (HRV) is the causative agent of severe dehydrating diarrhea in children under the age of five, resulting in up to 215,000 deaths each year. These deaths almost exclusively occur in low- and middle-income countries where vaccine efficacy is the lowest due to chronic malnutrition, gut dysbiosis, and concurrent enteric viral infection. Parenteral vaccines for HRV are particularly attractive as they avoid many of the concerns associated with currently used live oral vaccines. In this study, a two-dose intramuscular (IM) regimen of the trivalent, nanoparticle-based, nonreplicating HRV vaccine (trivalent S60-VP8*), utilizing the shell (S) domain of the capsid of norovirus as an HRV VP8* antigen display platform, was evaluated for immunogenicity and protective efficacy against P[6] and P[8] HRV using gnotobiotic pig models. A prime–boost strategy using one dose of the oral Rotarix® vaccine, followed by one dose of the IM trivalent nanoparticle vaccine was also evaluated. Both regimens were highly immunogenic in inducing serum virus neutralizing, IgG, and IgA antibodies. The two vaccine regimens failed to confer significant protection against diarrhea; however, the prime–boost regimen significantly shortened the duration of virus shedding in pigs challenged orally with the virulent Wa (G1P[8]) HRV and significantly shortened the mean duration of virus shedding, mean peak titer, and area under the curve of virus shedding after challenge with Arg (G4P[6]) HRV. Prime–boost-vaccinated pigs challenged with P[8] HRV had significantly higher P[8]-specific IgG antibody-secreting cells (ASCs) in the spleen post-challenge. Prime–boost-vaccinated pigs challenged with P[6] HRV had significantly higher numbers of P[6]- and P[8]-specific IgG ASCs in the ileum, as well as significantly higher numbers of P[8]-specific IgA ASCs in the spleen post-challenge. These results suggest the promise of and warrant further investigation into the oral priming and parenteral boosting strategy for future HRV vaccines.Instituto de VirologíaFil: Hensley, Casey. Virginia-Maryland College of Veterinary Medicine. Department of Biomedical Sciences and Pathobiology; Estados UnidosFil: Nyblade, Charlotte. Virginia-Maryland College of Veterinary Medicine. Department of Biomedical Sciences and Pathobiology; Estados UnidosFil: Zhou, Peng. Virginia-Maryland College of Veterinary Medicine. Department of Biomedical Sciences and Pathobiology; Estados UnidosFil: Parreño, Gladys Viviana. Virginia-Maryland College of Veterinary Medicine. Department of Biomedical Sciences and Pathobiology; Estados UnidosFil: Parreño, Gladys Viviana. Instituto Nacional de Tecnología Agropecuaria (INTA). INCUINTA. Instituto de Virologia e Innovaciones Tecnologicas (IVIT); ArgentinaFil: Parreño, Gladys Viviana. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ramesh, Ashwin. Virginia-Maryland College of Veterinary Medicine. Department of Biomedical Sciences and Pathobiology; Estados UnidosFil: Frazier, Annie. Virginia-Maryland College of Veterinary Medicine. Department of Biomedical Sciences and Pathobiology; Estados UnidosFil: Frazier, Maggie. Virginia-Maryland College of Veterinary Medicine. Department of Biomedical Sciences and Pathobiology; Estados UnidosFil: Garrison, Sarah. Virginia-Maryland College of Veterinary Medicine. Department of Biomedical Sciences and Pathobiology; Estados UnidosFil: Fantasia-Davis, Ariana. Virginia-Maryland College of Veterinary Medicine. Department of Biomedical Sciences and Pathobiology; Estados UnidosFil: Cai, Ruiqing. Virginia-Maryland College of Veterinary Medicine. Department of Biomedical Sciences and Pathobiology; Estados UnidosFil: Huang, Peng-Wei. Cincinnati Children’s Hospital Medical Center. Division of Infectious Diseases; Estados UnidosFil: Xia, Ming. Cincinnati Children’s Hospital Medical Center. Division of Infectious Diseases; Estados UnidosFil: Tan, Ming. Cincinnati Children’s Hospital Medical Center. Division of Infectious Diseases; Estados UnidosFil: Tan, Ming. University of Cincinnati College of Medicine. Department of Pediatrics; Estados UnidosFil: Yuan, Lijuan. Virginia-Maryland College of Veterinary Medicine. Department of Biomedical Sciences and Pathobiology; Estados Unido

Deep optoacoustic localization microangiography of ischemic stroke in mice

Super-resolution optoacoustic imaging of microvascular structures deep in mammalian tissues has so far been impeded by strong absorption from densely-packed red blood cells. Here we devised 5 µm biocompatible dichloromethane-based microdroplets exhibiting several orders of magnitude higher optical absorption than red blood cells at near-infrared wavelengths, thus enabling single-particle detection in vivo. We demonstrate non-invasive three-dimensional microangiography of the mouse brain beyond the acoustic diffraction limit (<20 µm resolution). Blood flow velocity quantification in microvascular networks and light fluence mapping was also accomplished. In mice affected by acute ischemic stroke, the multi-parametric multi-scale observations enabled by super-resolution and spectroscopic optoacoustic imaging revealed significant differences in microvascular density, flow and oxygen saturation in ipsi- and contra-lateral brain hemispheres. Given the sensitivity of optoacoustics to functional, metabolic and molecular events in living tissues, the new approach paves the way for non-invasive microscopic observations with unrivaled resolution, contrast and speed