Urban growth and decline occur every year and show changes in urban areas. Although various approaches to detect urban changes have been developed, they mainly use large-scale satellite imagery and ...socioeconomic factors in urban areas, which provides an overview of urban changes. However, since people explore places and notice changes daily at the street level, it would be useful to develop a method to identify urban changes at the street level and demonstrate whether urban growth or decline occurs there. Thus, this study seeks to use street-level panoramic images from Google Street View to identify urban changes and to develop a new way to evaluate the growth and decline of an urban area. After collecting Google Street View images year by year, we trained and developed a deep-learning model of an object detection process using the open-source software TensorFlow. By scoring objects and changes detected on a street from year to year, a map of urban growth and decline was generated for Midtown in Detroit, Michigan, USA. By comparing socioeconomic changes and the situations of objects and changes in Midtown, the proposed method is shown to be helpful for analyzing urban growth and decline by using year-by-year street view images.
By utilizing cognitive mapping and leveraging georeferenced text data, this paper aims to suggest a new visualization method that combines the advantages of both conventional and state-of-the-art ...research techniques to depict the collective identity of place in a single image. The study addressed two research questions: (1) Can crowd-sourced text data be utilized in representing place identity? (2) Can collective place identity be expressed in the form of a cognitive map? By confirming that text data gathered from social media effectively demonstrate people's behaviors and perceptions related to places, we propose a novel method to create a visual representation of urban identity-a "crowd-sourced cognitive map". In particular, to improve the conventional cognitive mapping method to depict the collective identity of a city, we draw cognitive maps of Bundang and Ilsan developed in the 1990s, as well as Songdo and Dongtan developed in the 2000s, just outside of the administrative boundaries of Seoul in Korea, through a computational method based on crowd-sourced opinions collected from social media. We open the possibility for the use of social media text data to capture the identity of cities and suggest a graphical image through which people without prior information could also easily apprehend the overall image of a city. The work in this paper is expected to provide a methodological technique for appropriate decision-making and the evaluation of urban identity to shape a more unique and imageable city.
Visibility represents the size of a space that people visually perceive. An isovist is a spatial concept and measure to illustrate visibility and its visual characteristics in the built environment. ...For measuring openness within an urban area, the isovist has been widely used as a representative measure. Since actual perception of urban space is based on the three-dimensional (3D) form, the conventional two-dimensional analysis of an isovist has been sought to extend to 3D isovist measures. In this paper, we define a new measure for a 3D urban environment that is directly applicable to a standard range sensor measurement. Upon developing volumetric and angular openness indexes in the three-dimensional coordinate systems, the findings in this paper emphasize that the viewing angle is critical in measuring 3D openness while closely connected to the range measurements. By mutually considering the angle and the range simultaneously, the proposed 3D isovist captures a viewpoint from pedestrians for more realistic openness evaluation. Additionally, we validate the proposed method in both simulated environments and real point cloud sensor data. The proposed, straightforward, point-based method is expected to be applicable to measure a real urban environment with a mobile mapping system with 3D point clouds and LiDAR.
•We developed an angle induced measure as a three-dimensional isovist motivated from a remote sensor measurement data.•We compared angle induced and volume measures to capture three-dimensional visibility.•The proposed three-dimensional openness measures were examined in various hypothetical environments simulating urban space.•We validated the proposed approach using both simulated model and real sensor data captured from the urban environment.
Abstract
Background
Cancer is a highly heterogeneous disease with varying responses to anti-cancer drugs. Although several attempts have been made to predict the anti-cancer therapeutic responses, ...there remains a great need to develop highly accurate prediction models of response to the anti-cancer drugs for clinical applications toward a personalized medicine. Patient derived xenografts (PDXs) are preclinical cancer models in which the tissue or cells from a patient’s tumor are implanted into an immunodeficient or humanized mouse. In the present study, we develop a bioinformatics analysis pipeline to build a predictive gene expression model (GEM) for cancer patients’ drug responses based on gene expression and drug activity data from PDX models.
Results
Drug sensitivity biomarkers were identified by performing an association analysis between gene expression levels and post-treatment tumor volume changes in PDX models. We built a drug response prediction model (called PDXGEM) in a random-forest algorithm by using a subset of the drug sensitvity biomarkers with concordant co-expression patterns between the PDXs and pretreatment cancer patient tumors. We applied the PDXGEM to several cytotoxic chemotherapies as well as targeted therapy agents that are used to treat breast cancer, pancreatic cancer, colorectal cancer, or non-small cell lung cancer. Significantly accurate predictions of PDXGEM for pathological response or survival outcomes were observed in extensive independent validations on multiple cancer patient datasets obtained from retrospective observational studies and prospective clinical trials.
Conclusion
Our results demonstrated the strong potential of using molecular profiles and drug activity data of PDX tumors in developing a clinically translatable predictive cancer biomarkers for cancer patients. The PDXGEM web application is publicly available at
http://pdxgem.moffitt.org
.
To prevent driver accidents in cities, local governments have established policies to limit city speeds and create child protection zones near schools. However, if the same policy is applied ...throughout a city, it can be difficult to obtain smooth traffic flows. A driver generally obtains visual information while driving, and this information is directly related to traffic safety. In this study, we propose a novel geometric visual model to measure drivers' visual perception and analyze the corresponding information using the line-of-sight method. Three-dimensional point cloud data are used to analyze on-site three-dimensional elements in a city, such as roadside trees and overpasses, which are normally neglected in urban spatial analyses. To investigate drivers' visual perceptions of roads, we have developed an analytic model of three types of visual perception. By using this proposed method, this study creates a risk-level map according to the driver's visual perception degree in Pangyo, South Korea. With the point cloud data from Pangyo, it is possible to analyze actual urban forms such as roadside trees, building shapes, and overpasses that are normally excluded from spatial analyses that use a reconstructed virtual space.
Breast cancer-related leptomeningeal disease (BC-LMD) is a dire diagnosis for 5-8% of patients with breast cancer (BC). We conducted a retrospective review of BC-LMD patients diagnosed at Moffitt ...Cancer Center from 2011 to 2020, to determine the changing incidence of BC-LMD, factors which are associated with the progression of BC CNS metastasis to BC-LMD, and factors which are associated with OS for patients with BC-LMD.
Patients with BC and brain/spinal metastatic disease were identified. For those who eventually developed BC-LMD, we used Kaplan-Meier survival curve, log-rank test, univariable, and multivariate Cox proportional hazards regression model to identify factors affecting time from CNS metastasis to BC-LMD and OS.
128 cases of BC-LMD were identified. The proportion of BC-LMD to total BC patients was higher between 2016 and 2020 when compared to 2011-2015. Patients with HR+ or HER2 + BC experienced longer times between CNS metastasis and LMD than patients with triple-negative breast cancer (TNBC). Systemic therapy and whole-brain radiation therapy (WBRT) was associated with prolonged progression to LMD in all patients. Hormone therapy in patients with HR + BC were associated with a delayed BC-CNS metastasis to LMD progression. Lapatinib treatment was associated with a delayed progression to LMD in patients with HER2 + BC. Patients with TNBC-LMD had shorter OS compared to those with HR + and HER2 + BC-LMD. Systemic therapy, intrathecal (IT) therapy, and WBRT was associated with prolonged survival for all patients. Lapatinib and trastuzumab therapy was associated with improved OS in patients with HER2 + BC-LMD.
Increasing rates of BC-LMD provide treatment challenges and opportunities for clinical trials. Prospective trials testing lapatinib and/or similar tyrosine kinase inhibitors, IT therapies, and combination treatments are urgently needed.
Since advances in next-generation sequencing (NGS) technique enabled to investigate uncultured microbiota and their genomes in unbiased manner, many microbiome researches have been reporting strong ...evidences for close links of microbiome to human health and disease. Bioinformatic and statistical analysis of NGS-based microbiome data are essential components in those microbiome researches to explore the complex composition of microbial community and understand the functions of community members in relation to host and environment. This chapter introduces bioinformatic analysis methods that generate taxonomy and functional feature count table along with phylogenetic tree from raw NGS microbiome data and then introduce statistical methods and machine learning approaches for analyzing the outputs of the bioinformatic analysis to infer the biodiversity of a microbial community and unravel host-microbiome association. Understanding the advantages and limitations of the analysis methods will help readers use the methods correctly in microbiome data analysis and may give a new opportunity to develop new analytic techniques for microbiome research.
This study aimed to develop a building-integrated photovoltaic (BIPV) device and optimal control methods that increase the photovoltaic (PV) efficiency and visual comfort of the indoor space. A ...louver-type PV-integrated shading device was suggested and an artificial neural networks (ANN) model was developed to predict PV electricity output, work plane illuminance, and daylight glare index (DGI). The slat tilt angle of the shading device was controlled to maximize PV electricity output based on three different strategies: one without visual comfort constraints, and the other two with visual comfort constraints: work plane illuminance and DGI. Optimal tilt angle was calculated using predictions of the ANN. Experiments were conducted to verify the system modeling and to evaluate the performance of the shading device. Experiment results revealed that the ANN model successfully predicted the PV output, work plane illuminance, and DGI. The PV-integrated shading device was more efficient in producing electricity than the conventional wall-mount PV systems, the control method without visual comfort constraints was most efficient in generating electricity than the other two with such constraints, and excluding the constraints resulted in less comfortable visual environment and reduced energy benefit. From the results analysis, it can be concluded that based on the accurate predictions, the PV-integrated shading device controlled using the proposed methods produced more electricity compared to the wall-mount counterpart.
Though Isoimperatorin from
is known to have antiviral, antidiabetic, anti-inflammatory and antitumor effects, its underlying antitumor mechanism remains elusive so far. Hence, the apoptotic mechanism ...of Isoimperatorin was explored in hepatocellular carcinomas (HCCs). In this study, Isoimperatorin inhibited the viability of Huh7 and Hep3B HCCs and increased the subG1 apoptotic portion and also abrogated the expression of pro-poly-ADP ribose polymerase (pro-PARP) and pro-caspase 3 in Huh7 and Hep3B cells. Also, Isoimperatorin abrogated the expression of cyclin D1, cyclin E1, CDK2, CDK4, CDK6 and increased p21 as G1 phase arrest-related proteins in Huh7 and Hep3B cells. Interestingly, Isoimperatorin reduced the expression and binding of c-Myc and Sirtuin 1 (SIRT1) by Immunoprecipitation (IP), with a binding score of 0.884 in Huh7 cells. Furthermore, Isoimperatorin suppressed the overexpression of c-Myc by the proteasome inhibitor MG132 and also disturbed cycloheximide-treated c-Myc stability in Huh7 cells. Overall, these findings support the novel evidence that the pivotal role of c-Myc and SIRT1 is critically involved in Isoimperatorin-induced apoptosis in HCCs as potent molecular targets in liver cancer therapy.
In our recent study, most non-small-lung cancer (NSCLC) tumor specimens harbored viral DNA but it was absent in non-neoplastic lung. However, their targets and roles in the tumor cells remain poorly ...understood. We analyzed gene expression microarrays to identify genes and pathways differentially altered between virus-infected and uninfected NSCLC tumors.
Gene expression microarrays of 30 primary and 9 metastatic NSCLC patients were preprocessed through a series of quality control analyses. Linear Models for Microarray Analysis and Gene Set Enrichment Analysis were used to assess differential expression.
Various genes and gene sets had significantly altered expressions between virus-infected and uninfected NSCLC tumors. Notably, 22 genes on the viral carcinogenesis pathway were significantly overexpressed in virus-infected primary tumors, along with three oncogenic gene sets. A total of 12 genes, as well as seven oncogenic and 133 immunologic gene sets, were differentially altered in squamous cell carcinomas, depending on the virus. In adenocarcinoma, 14 differentially expressed genes (DEGs) were identified, but no oncogenic and immunogenic gene sets were significantly altered. In bronchioloalveolar carcinoma, several genes were highly overexpressed in virus-infected specimens, but not statistically significant. Only five of 69 DEGs (7.2%) from metastatic tumor analysis overlapped with 1527 DEGs from the primary tumor analysis, indicating differences in host cellular targets and the viral impact between primary and metastatic NSCLC.
The differentially expressed genes and gene sets were distinctive among infected viral types, histological subtypes, and metastatic disease status of NSCLC. These results support the hypothesis that tumor viruses play a role in NSCLC by regulating host genes in tumor cells during NSCLC differentiation and progression.
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