STDA-Meta: A Meta-Learning Framework for Few-Shot Traffic Prediction

As the development of cities, traffic congestion becomes an increasingly pressing issue, and traffic prediction is a classic method to relieve that issue. Traffic prediction is one specific application of spatio-temporal prediction learning, like taxi scheduling, weather prediction, and ship trajectory prediction. Against these problems, classical spatio-temporal prediction learning methods including deep learning, require large amounts of training data. In reality, some newly developed cities with insufficient sensors would not hold that assumption, and the data scarcity makes predictive performance worse. In such situation, the learning method on insufficient data is known as few-shot learning (FSL), and the FSL of traffic prediction remains challenges. On the one hand, graph structures' irregularity and dynamic nature of graphs cannot hold the performance of spatio-temporal learning method. On the other hand, conventional domain adaptation methods cannot work well on insufficient training data, when transferring knowledge from different domains to the intended target domain.To address these challenges, we propose a novel spatio-temporal domain adaptation (STDA) method that learns transferable spatio-temporal meta-knowledge from data-sufficient cities in an adversarial manner. This learned meta-knowledge can improve the prediction performance of data-scarce cities. Specifically, we train the STDA model using a Model-Agnostic Meta-Learning (MAML) based episode learning process, which is a model-agnostic meta-learning framework that enables the model to solve new learning tasks using only a small number of training samples. We conduct numerous experiments on four traffic prediction datasets, and our results show that the prediction performance of our model has improved by 7\% compared to baseline models on the two metrics of MAE and RMSE

Identification of discharge regimes of cyclone dipleg-trickle valve system based on pressure fluctuation profiles

An experiment was conducted on the Φ150mm×5000mmcyclone dipleg-trickle valve setup, which was focused on analyzing the discharge characteristics of trickle valve of cyclone dipleg by means of the dynamic pressure measurement. The effects of two operating parameters, negative pressure drop (0~11kPa) and solids flux rate (0~50 kg/m2.s), on the discharge patterns were investigated. The experimental results show that there are two kinds of discharge patterns in the trickle valve. One is continuous trickling discharge at low negative pressure drop and high solids flux rate, which is characterized by valve plate opening continuously, and the measured pressure with high frequency and low amplitude. The other is intermittent periodic dumping discharge at high negative pressure drop and low solids flux rate, which has the properties of valve plate opening interval, and the measured pressure with low frequency and high amplitude. The two discharge patterns could transform each other as varying the negative pressure drop or solids flux rate. The discharge regime map was proposed based on the experimental data, which is related to the negative. Please click Additional Files below to see the full abstract

Resistance of Enterococcus spp. in Dust From Farm Animal Houses: A Retrospective Study

In a retrospective study, the antimicrobial susceptibility of Enterococcus spp. isolated from stored sedimentation dust samples from cattle, pig and poultry barns to 16 antibiotics was determined using a microdilution test. The resistance phenotypes of 70 isolates from different timespans (8 from the 1980s, 15 from the 1990s, 43 from the 2000s and 4 from 2015) were determined. Resistant enterococci were detected in samples from all time periods. Resistances to three or more antibiotics occurred in 69 percent of all isolates. The oldest multidrug resistant isolate was an Enterococcus faecium obtained from a 35-year-old pig barn dust sample. No correlations (ρ = 0.16, p = 0.187) were found between the age of isolates and the number of resistances. Instead, the number of resistances was associated with the origin of the isolates. An exact logistic conditional regression analysis showed significant differences in resistance to ciprofloxacin, erythromycin, penicillin and tylosin between isolates from different animal groups. Interestingly, we isolated ciprofloxacin-resistant E. faecium from pig barn dust before fluoroquinolones were introduced into the market for use in animal husbandry. In conclusion, dust from farm animal houses is a reservoir and carrier of multidrug-resistant Enterococcus spp. People working in barns are unavoidably exposed to these bacteria. Furthermore, it can be hypothesized that emissions from barns of intensive livestock farming contaminate the environment with multidrug resistant enterococci

Oncoproteins E6 and E7 upregulate topoisomerase I to activate the cGAS-PD-L1 pathway in cervical cancer development

Background: Cervical cancer (CC) stands as a significant health threat to women globally, with high-risk human papillomaviruses as major etiologic agents. The DNA damage repair (DDR) protein topoisomerase I (TOP1) has been linked to various cancers, yet its distinct roles and mechanisms in CC are not fully elucidated.Methods: We investigated TOP1 expression in cervical intraepithelial neoplasia (CIN) and CC tissues utilizing qRT-PCR and IHC, correlating findings with patient prognosis. Subsequent knockdown studies were performed in vitro and in vivo to evaluate the influence of TOP1 on tumor growth, DNA repair, and inflammatory responses.Results: TOP1 was highly expressed in CIN and CC, negatively correlating with patient prognosis. Inhibition of TOP1 impeded CC cell growth and disrupted DNA repair. TOP1 was shown to regulate tumor-promoting inflammation and programmed death-ligand 1 (PD-L1) production in a cGAS-dependent manner. HPV oncoproteins E6 and E7 upregulated TOP1 and activated the cGAS-PD-L1 pathway.Conclusions: TOP1 acts as a DNA repair mediator, promoting CC development and immune evasion. Targeting the TOP1-cGAS-PD-L1 axis could be a potential therapeutic strategy for CC

Guinea Pig Model for Evaluating the Potential Public Health Risk of Swine and Avian Influenza Viruses

BACKGROUND: The influenza viruses circulating in animals sporadically transmit to humans and pose pandemic threats. Animal models to evaluate the potential public health risk potential of these viruses are needed. METHODOLOGY/PRINCIPAL FINDINGS: We investigated the guinea pig as a mammalian model for the study of the replication and transmission characteristics of selected swine H1N1, H1N2, H3N2 and avian H9N2 influenza viruses, compared to those of pandemic (H1N1) 2009 and seasonal human H1N1, H3N2 influenza viruses. The swine and avian influenza viruses investigated were restricted to the respiratory system of guinea pigs and shed at high titers in nasal tracts without prior adaptation, similar to human strains. None of the swine and avian influenza viruses showed transmissibility among guinea pigs; in contrast, pandemic (H1N1) 2009 virus transmitted from infected guinea pigs to all animals and seasonal human influenza viruses could also horizontally transmit in guinea pigs. The analysis of the receptor distribution in the guinea pig respiratory tissues by lectin histochemistry indicated that both SAα2,3-Gal and SAα2,6-Gal receptors widely presented in the nasal tract and the trachea, while SAα2,3-Gal receptor was the main receptor in the lung. CONCLUSIONS/SIGNIFICANCE: We propose that the guinea pig could serve as a useful mammalian model to evaluate the potential public health threat of swine and avian influenza viruses