Minimalist Traffic Prediction: Linear Layer Is All You Need

Traffic prediction is essential for the progression of Intelligent Transportation Systems (ITS) and the vision of smart cities. While Spatial-Temporal Graph Neural Networks (STGNNs) have shown promise in this domain by leveraging Graph Neural Networks (GNNs) integrated with either RNNs or Transformers, they present challenges such as computational complexity, gradient issues, and resource-intensiveness. This paper addresses these challenges, advocating for three main solutions: a node-embedding approach, time series decomposition, and periodicity learning. We introduce STLinear, a minimalist model architecture designed for optimized efficiency and performance. Unlike traditional STGNNs, STlinear operates fully locally, avoiding inter-node data exchanges, and relies exclusively on linear layers, drastically cutting computational demands. Our empirical studies on real-world datasets confirm STLinear's prowess, matching or exceeding the accuracy of leading STGNNs, but with significantly reduced complexity and computation overhead (more than 95% reduction in MACs per epoch compared to state-of-the-art STGNN baseline published in 2023). In summary, STLinear emerges as a potent, efficient alternative to conventional STGNNs, with profound implications for the future of ITS and smart city initiatives.Comment: 9 page

A peptide inhibitor of exportin1 blocks shuttling of the adenoviral E1B 55 kDa protein but not export of viral late mRNAs

AbstractThe human subgroup C adenoviral E1B 55 kDa and E4 Orf6 proteins are required for efficient nuclear export of viral late mRNAs, but the cellular pathway that mediates such export has not been identified. As a first step to develop a general approach to address this issue, we have assessed the utility of cell-permeable peptide inhibitors of cellular export receptors. As both E1B and E4 proteins have been reported to contain a leucine-rich nuclear export signal (NES), we synthesized a cell-permeable peptide containing such an NES. This peptide induced substantial inhibition of export of the E1B protein, whereas a control, non-functional peptide did not. However, under the same conditions, the NES peptide had no effect on export of viral late mRNAs. These observations establish that viral late mRNAs are not exported by exportin1, as well as the value of peptide inhibitors in investigation of mRNA export regulation in adenovirus-infected cells

Tumor Necrosis Factor- α

Neonatal sepsis (NS) is an important cause of mortality in newborns and life-threatening disorder in infants. The meta-analysis was performed to investigate the diagnosis value of tumor necrosis factor-α (TNF-α) test in NS. Our collectible studies were searched from PUBMED, EMBASE, and the Cochrane Library between March 1994 and August 2013. Accordingly, 347 studies were collected totally, in which 15 articles and 23 trials were selected to study the NS in our meta-analysis. The TNF-α test showed moderate accuracy of the diagnosis of NS both in early-onset neonatal sepsis (sensitivity = 0.66, specificity = 0.76, Q* = 0.74) and in late-onset neonatal sepsis (sensitivity = 0.68, specificity = 0.89, Q* = 0.87). We also found the northern hemisphere group in the test has higher sensitivity (0.84) and specificity (0.83). A diagnostic OR analysis found that the study population may be the major reason for the heterogeneity. Accordingly, we suggest that TNF-α is also a valuable marker in the diagnosis of NS

Quarterly GDP forecast based on coupled economic and energy feature WA-LSTM model

Existing macroeconomic forecasting methods primarily focus on the characteristics of economic data, but they overlook the energy-related features concealed behind these economic characteristics, which may lead to inaccurate GDP predictions. Therefore, this paper meticulously analyzes the relationship between energy big data and economic data indicators, explores the coupling feature mining of energy big data and economic data, and constructs features coupling economic and energy data. Targeting the nonlinear variation coupling features in China’s quarterly GDP data and using the long short-term memory (LSTM) neural network model based on deep learning, we employ wavelet analysis technology (WA) to decompose selected macroeconomic variables and construct a prediction model combining LSTM and WA, which is further compared with multiple benchmark models. The research findings show that, in terms of quarterly GDP data prediction, the combined deep learning model and wavelet analysis significantly outperform other methods. When processing structurally complex, nonlinear, and multi-variable data, the LSTM and WA combined prediction model demonstrate better generalization capabilities, with its prediction accuracy generally surpassing other benchmark models

Observation of Fungi, Bacteria, and Parasites in Clinical Skin Samples Using Scanning Electron Microscopy

This chapter highlights the description of the clinical manifestation and its pathogen and the host tissue damage observed under the Scanning Electron Microscope, which helps the clinician to understand the pathogen’s superstructure, the change of host subcell structure, and the laboratory workers to understand the clinical characteristics of pathogen-induced human skin lesions, to establish a two-way learning exchange database with vivid image

A novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray data

<p>Abstract</p> <p>Background</p> <p>Identification of biomarkers among thousands of genes arrayed for disease classification has been the subject of considerable research in recent years. These studies have focused on disease classification, comparing experimental groups of effected to normal patients. Related experiments can be done to identify tissue-restricted biomarkers, genes with a high level of expression in one tissue compared to other tissue types in the body.</p> <p>Results</p> <p>In this study, cartilage was compared with ten other body tissues using a two color array experimental design. Thirty-seven probe sets were identified as cartilage biomarkers. Of these, 13 (35%) have existing annotation associated with cartilage including several well-established cartilage biomarkers. These genes comprise a useful database from which novel targets for cartilage biology research can be selected. We determined cartilage specific Z-scores based on the observed M to classify genes with Z-scores ≥ 1.96 in all ten cartilage/tissue comparisons as cartilage-specific genes.</p> <p>Conclusion</p> <p>Quantile regression is a promising method for the analysis of two color array experiments that compare multiple samples in the absence of biological replicates, thereby limiting quantifiable error. We used a nonparametric approach to reveal the relationship between percentiles of M and A, where M is log₂(R/G) and A is 0.5 log₂(RG) with R representing the gene expression level in cartilage and G representing the gene expression level in one of the other 10 tissues. Then we performed linear quantile regression to identify genes with a cartilage-restricted pattern of expression.</p

Residues 318 and 323 in capsid protein are involved in immune circumvention of the atypical epizootic infection of infectious bursal disease virus

Recently, atypical infectious bursal disease (IBD) caused by a novel variant infectious bursal disease virus (varIBDV) suddenly appeared in immunized chicken flocks in East Asia and led to serious economic losses. The epizootic varIBDV can partly circumvent the immune protection of the existing vaccines against the persistently circulating very virulent IBDV (vvIBDV), but its mechanism is still unknown. This study proved that the neutralizing titer of vvIBDV antiserum to the epizootic varIBDV reduced by 7.0 log2, and the neutralizing titer of the epizootic varIBDV antiserum to vvIBDV reduced by 3.2 log2. In addition, one monoclonal antibody (MAb) 2-5C-6F had good neutralizing activity against vvIBDV but could not well recognize the epizootic varIBDV. The epitope of the MAb 2-5C-6F was identified, and two mutations of G318D and D323Q of capsid protein VP2 occurred in the epizootic varIBDV compared to vvIBDV. Subsequently, the indirect immunofluorescence assay based on serial mutants of VP2 protein verified that residue mutations 318 and 323 influenced the recognition of the epizootic varIBDV and vvIBDV by the MAb 2-5C-6F, which was further confirmed by the serial rescued mutated virus. The following cross-neutralizing assay directed by MAb showed residue mutations 318 and 323 also affected the neutralization of the virus. Further data also showed that the mutations of residues 318 and 323 of VP2 significantly affected the neutralization of the IBDV by antiserum, which might be deeply involved in the immune circumvention of the epizootic varIBDV in the vaccinated flock. This study is significant for the comprehensive prevention and control of the emerging varIBDV

A Comparative Study of Systolic and Diastolic Mechanical Synchrony in Canine, Primate, and Healthy and Failing Human Hearts.

Aim: Mechanical dyssynchrony (MD) is associated with heart failure (HF) and may be prognostically important in cardiac resynchronization therapy (CRT). Yet, little is known about its patterns in healthy or diseased hearts. We here investigate and compare systolic and diastolic MD in both right (RV) and left ventricles (LV) of canine, primate and healthy and failing human hearts. Methods and Results: RV and LV mechanical function were examined by pulse-wave Doppler in 15 beagle dogs, 59 rhesus monkeys, 100 healthy human subjects and 39 heart failure (HF) patients. This measured RV and LV pre-ejection periods (RVPEP and LVPEP) and diastolic opening times (Q-TVE and Q-MVE). The occurrence of right (RVMDs) and left ventricular systolic mechanical delay (LVMDs) was assessed by comparing RVPEP and LVPEP values. That of right (RVMDd) and left ventricular diastolic mechanical delay (LVMDd) was assessed from the corresponding diastolic opening times (Q-TVE and Q-MVE). These situations were quantified by values of interventricular systolic (IVMDs) and diastolic mechanical delays (IVMDd), represented as positive if the relevant RV mechanical events preceded those in the LV. Healthy hearts in all species examined showed greater LV than RV delay times and therefore positive IVMDs and IVMDd. In contrast a greater proportion of the HF patients showed both markedly increased IVMDs and negative IVMDd, with diastolic mechanical asynchrony negatively correlated with LVEF. Conclusion: The present IVMDs and IVMDd findings have potential clinical implications particularly for personalized setting of parameter values in CRT in individual patients to achieve effective treatment of HF