Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints

Accurate understanding and prediction of human behaviors are critical prerequisites for autonomous vehicles, especially in highly dynamic and interactive scenarios such as intersections in dense urban areas. In this work, we aim at identifying crossing pedestrians and predicting their future trajectories. To achieve these goals, we not only need the context information of road geometry and other traffic participants but also need fine-grained information of the human pose, motion and activity, which can be inferred from human keypoints. In this paper, we propose a novel multi-task learning framework for pedestrian crossing action recognition and trajectory prediction, which utilizes 3D human keypoints extracted from raw sensor data to capture rich information on human pose and activity. Moreover, we propose to apply two auxiliary tasks and contrastive learning to enable auxiliary supervisions to improve the learned keypoints representation, which further enhances the performance of major tasks. We validate our approach on a large-scale in-house dataset, as well as a public benchmark dataset, and show that our approach achieves state-of-the-art performance on a wide range of evaluation metrics. The effectiveness of each model component is validated in a detailed ablation study.Comment: ICRA 202

Effect of the H1N1 Influenza Pandemic on the Incidence of Epidemic Keratoconjunctivitis and on Hygiene Behavior: A Cross-Sectional Study

Background: EKC is transmitted chiefly by direct hand contact. It is suspected that the 2009/2010 influenza pandemic influenced hand washing. This study aims to examine the relationship between the 2009/2010 H1N1 influenza pandemic and hygiene behavior. Methods: We compared the EKC prevalence trends before, during and after the 2009/2010 influenza pandemic by using a t-test comparison of EKC sentinel surveillance. Results: During the pre-pandemic period, the incidence of EKC increased from the 21st to the 44th week each year. However, during the pandemic period in 2009, there was no epidemic peak. In the post-pandemic period, the epidemic curve was similar to that in the pre-pandemic period. Compared to the pre-pandemic period, the total number of EKC patients during the pandemic period showed a decrease of 44.9 % (t value = 27.23, p = 0.002). Comparing the pre-pandemic and pandemic periods by age group, we found there to be a significant decrease in the number of EKC patients for all age groups (24.12#t value#27.23, all P,0.05). This finding was most evident in the teenage group (62%) compared to the other age groups (decreases of 29 to 44%). Conclusions: A continuing effort should be made to educate the public on basic infection prevention behaviors in th

Mesenchymal-epithelial signalling in tumour microenvironment: role of high-mobility group Box 1.

Glucose deprivation, hypoxia and acidosis are characteristic features of the central core of most solid tumours. Myofibroblasts are stromal cells present in many such solid tumours, including those of the colon, and are known to be involved in all stages of tumour progression. HMGB1 is a nuclear protein with an important role in nucleosome stabilisation and gene transcription; it is also released from immune cells and is involved in the inflammatory process. We report that the microenvironmental condition of glucose deprivation is responsible for the active release of HMGB1 from various types of cancer cell lines (HT-29, MCF-7 and A549) under normoxic conditions. Recombinant HMGB1 (10 ng/ml) triggered proliferation in myofibroblast cells via activation of PI3K and MEK1/2. Conditioned medium collected from glucose-deprived HT-29 colon cancer cells stimulated the migration and invasion of colonic myofibroblasts, and these processes were significantly inhibited by immunoneutralising antibodies to HMGB1, RAGE and TLR4, together with specific inhibitors of PI3K and MEK1/2. Our data suggest that HMGB1 released from cancer cells under glucose deprivation is involved in stimulating colonic myofibroblast migration and invasion and that this occurs through the activation of RAGE and TLR4, resulting in the activation of the MAPK and PI3K signalling pathways. Thus, HMGB1 might be released by cancer cells in areas of low glucose in solid tumours with the resulting activation of myofibroblasts and is a potential therapeutic target to inhibit solid tumour growth

Application of two machine learning algorithms to genetic association studies in the presence of covariates

BACKGROUND: Population-based investigations aimed at uncovering genotype-trait associations often involve high-dimensional genetic polymorphism data as well as information on multiple environmental and clinical parameters. Machine learning (ML) algorithms offer a straightforward analytic approach for selecting subsets of these inputs that are most predictive of a pre-defined trait. The performance of these algorithms, however, in the presence of covariates is not well characterized. METHODS AND RESULTS: In this manuscript, we investigate two approaches: Random Forests (RFs) and Multivariate Adaptive Regression Splines (MARS). Through multiple simulation studies, the performance under several underlying models is evaluated. An application to a cohort of HIV-1 infected individuals receiving anti-retroviral therapies is also provided. CONCLUSION: Consistent with more traditional regression modeling theory, our findings highlight the importance of considering the nature of underlying gene-covariate-trait relationships before applying ML algorithms, particularly when there is potential confounding or effect mediation

Geographical gradient of the <em>eIF4E</em> alleles conferring resistance to potyviruses in pea (<em>Pisum</em>) germplasm

<div><p>Background</p><p>The eukaryotic translation initiation factor 4E was shown to be involved in resistance against several potyviruses in plants, including pea. We combined our knowledge of pea germplasm diversity with that of the <i>eIF4E</i> gene to identify novel genetic diversity.</p><p>Methodology/Principal findings</p><p>Germplasm of 2803 pea accessions was screened for <i>eIF4E</i> intron 3 length polymorphism, resulting in the detection of four <i>eIF4E^A-B-C-S</i> variants, whose distribution was geographically structured. The <i>eIF4E^A</i> variant conferring resistance to the P1 PSbMV pathotype was found in 53 accessions (1.9%), of which 15 were landraces from India, Afghanistan, Nepal, and 7 were from Ethiopia. A newly discovered variant, <i>eIF4E^B</i>, was present in 328 accessions (11.7%) from Ethiopia (29%), Afghanistan (23%), India (20%), Israel (25%) and China (39%). The <i>eIF4E^C</i> variant was detected in 91 accessions (3.2% of total) from India (20%), Afghanistan (33%), the Iberian Peninsula (22%) and the Balkans (9.3%). The <i>eIF4E^S</i> variant for susceptibility predominated as the wild type. Sequencing of 73 samples, identified 34 alleles at the whole gene, 26 at cDNA and 19 protein variants, respectively. Fifteen alleles were virologically tested and 9 alleles (<i>eIF4E^{A-1-2-3-4-5-6-7}</i>, <i>eIF4E^B-1</i>, <i>eIF4E^C-2</i>) conferred resistance to the P1 PSbMV pathotype.</p><p>Conclusions/Significance</p><p>This work identified novel <i>eIF4E</i> alleles within geographically structured pea germplasm and indicated their independent evolution from the susceptible <i>eIF4E^S1</i> allele. Despite high variation present in wild <i>Pisum</i> accessions, none of them possessed resistance alleles, supporting a hypothesis of distinct mode of evolution of resistance in wild as opposed to crop species. The Highlands of Central Asia, the northern regions of the Indian subcontinent, Eastern Africa and China were identified as important centers of pea diversity that correspond with the diversity of the pathogen. The series of alleles identified in this study provides the basis to study the co-evolution of potyviruses and the pea host.</p></div

Evasion of anti-growth signaling: a key step in tumorigenesis and potential target for treatment and prophylaxis by natural compounds

The evasion of anti-growth signaling is an important characteristic of cancer cells. In order to continue to proliferate, cancer cells must somehow uncouple themselves from the many signals that exist to slow down cell growth. Here, we define the anti-growth signaling process, and review several important pathways involved in growth signaling: p53, phosphatase and tensin homolog (PTEN), retinoblastoma protein (Rb), Hippo, growth differentiation factor 15 (GDF15), AT-rich interactive domain 1A (ARID1A), Notch, insulin-like growth factor (IGF), and Krüppel-like factor 5 (KLF5) pathways. Aberrations in these processes in cancer cells involve mutations and thus the suppression of genes that prevent growth, as well as mutation and activation of genes involved in driving cell growth. Using these pathways as examples, we prioritize molecular targets that might be leveraged to promote anti-growth signaling in cancer cells. Interestingly, naturally-occurring phytochemicals found in human diets (either singly or as mixtures) may promote anti-growth signaling, and do so without the potentially adverse effects associated with synthetic chemicals. We review examples of naturally-occurring phytochemicals that may be applied to prevent cancer by antagonizing growth signaling, and propose one phytochemical for each pathway. These are: epigallocatechin-3-gallate (EGCG) for the Rb pathway, luteolin for p53, curcumin for PTEN, porphyrins for Hippo, genistein for GDF15, resveratrol for ARID1A, withaferin A for Notch and diguelin for the IGF1-receptor pathway. The coordination of anti-growth signaling and natural compound studies will provide insight into the future application of these compounds in the clinical setting

Production of phi mesons at mid-rapidity in sqrt(s_NN) = 200 GeV Au+Au collisions at RHIC

We present the first results of meson production in the K^+K^- decay channel from Au+Au collisions at sqrt(s_NN) = 200 GeV as measured at mid-rapidity by the PHENIX detector at RHIC. Precision resonance centroid and width values are extracted as a function of collision centrality. No significant variation from the PDG accepted values is observed. The transverse mass spectra are fitted with a linear exponential function for which the derived inverse slope parameter is seen to be constant as a function of centrality. These data are also fitted by a hydrodynamic model with the result that the freeze-out temperature and the expansion velocity values are consistent with the values previously derived from fitting single hadron inclusive data. As a function of transverse momentum the collisions scaled peripheral.to.central yield ratio RCP for the is comparable to that of pions rather than that of protons. This result lends support to theoretical models which distinguish between baryons and mesons instead of particle mass for explaining the anomalous proton yield.Comment: 326 authors, 24 pages text, 23 figures, 6 tables, RevTeX 4. To be submitted to Physical Review C as a regular article. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.htm