Modeling the Time Spent at Points of Interest Based on Google Popular Times

Location-based applications are increasingly popular as smartphones with navigation capabilities are becoming more prevalent. Analyzing the time spent by visitors at Points of Interests (POIs) is crucial in various fields, such as urban planning, tourism, marketing, and transportation, as it provides insights into human behavior and decision-making. However, collecting a large sample of behavioral data by using traditional survey methods is expensive and complicated. To address this challenge, this study explores the use of crowdsourcing tools, specifically Google Popular Times (GPT), as an alternative source of information to predict the time spent at POIs. The research applies a robust regression model to analyze the data obtained from GPT. The popularity trends of the different POI categories are used to indicate the peak hours of the time spent in the city of Budapest. Non-spatial parameters such as the rating, the number of reviewers, and the category of the POIs are utilized. Furthermore, a Geographic Information System (GIS) is applied to extract the spatial parameters such as the security and safety levels, the availability of car parking, and public transport (PT) stations. The robust linear models are statistically significant based on the p-values, thus indicating a strong relationship between the independent variables and the time spent at POIs. The weekday and weekend models present 69.5% and 73.9% of the variance in the time spent at POIs, respectively. Furthermore, it is demonstrated that the visitors’ behavior is strongly affected by the category of the POIs variable. This study shows how GPT can be utilized to better understand, analyze, and forecast people’s behavior. The solution presented in this study can serve as an essential support of activity-based models, where the time spent is a crucial parameter for scheduling and optimizing activity chains

Alteration in the Wnt microenvironment directly regulates molecular events leading to pulmonary senescence

In the aging lung, the lung capacity decreases even in the absence of diseases. The progenitor cells of the distal lung, the alveolar type II cells (ATII), are essential for the repair of the gas-exchange surface. Surfactant protein production and survival of ATII cells are supported by lipofibroblasts that are peroxisome proliferator-activated receptor gamma (PPARγ)-dependent special cell type of the pulmonary tissue. PPARγ levels are directly regulated by Wnt molecules; therefore, changes in the Wnt microenvironment have close control over maintenance of the distal lung. The pulmonary aging process is associated with airspace enlargement, decrease in the distal epithelial cell compartment and infiltration of inflammatory cells. qRT–PCR analysis of purified epithelial and nonepithelial cells revealed that lipofibroblast differentiation marker parathyroid hormone-related protein receptor (PTHrPR) and PPARγ are reduced and that PPARγ reduction is regulated by Wnt4 via a β-catenin-dependent mechanism. Using a human in vitro 3D lung tissue model, a link was established between increased PPARγ and pro-surfactant protein C (pro-SPC) expression in pulmonary epithelial cells. In the senile lung, both Wnt4 and Wnt5a levels increase and both Wnt-s increase myofibroblast-like differentiation. Alteration of the Wnt microenvironment plays a significant role in pulmonary aging. Diminished lipo- and increased myofibroblast-like differentiation are directly regulated by specific Wnt-s, which process also controls surfactant production and pulmonary repair mechanisms

Down-Regulation of Canonical and Up-Regulation of Non-Canonical Wnt Signalling in the Carcinogenic Process of Squamous Cell Lung Carcinoma

<div><p>The majority of lung cancers (LC) belong to the non-small cell lung carcinoma (NSCLC) type. The two main NSCLC sub-types, namely adenocarcinoma (AC) and squamous cell carcinoma (SCC), respond differently to therapy. Whereas the link between cigarette smoke and lung cancer risk is well established, the relevance of non-canonical Wnt pathway up-regulation detected in SCC remains poorly understood. The present study was undertaken to investigate further the molecular events in canonical and non-canonical Wnt signalling during SCC development. A total of 20 SCC and AC samples with matched non-cancerous controls were obtained after surgery. TaqMan array analysis confirmed up-regulation of non-canonical Wnt5a and Wnt11 and identified down-regulation of canonical Wnt signalling in SCC samples. The molecular changes were tested in primary small airway epithelial cells (SAEC) and various lung cancer cell lines (e.g. A549, H157, etc). Our studies identified Wnt11 and Wnt5a as regulators of cadherin expression and potentiated relocation of β-catenin to the nucleus as an important step in decreased cellular adhesion. The presented data identifies additional details in the regulation of SCC that can aid identification of therapeutic drug targets in the future.</p> </div

Predicted molecular pathway interactions during SCC development.

<p>Predicted molecular pathway interactions during SCC development.</p

Effects of β-catenin inhibition on cadherin gene expression.

<p>Suppression of canonical Wnt signalling in SAEC using 1 µg/ml IWR-1 inhibitor or DMSO as diluent control. Gene expression of non-treated SAEC was used as reference. Note the increased E-cadherin and decreased N-cadherin mRNA expressions. (The results are representative of three independent experiments where SAEC was used from three individual donors of different ages).</p

Localization of β-catenin. A:

<p>Immunofluorescent staining of SAEC. <b>B</b>: Immunofluorescent staining of normal A549. <b>C</b>: Immunofluorescent staining of Wnt11 overexpressing A549. <b>D</b>: Immunofluorescent staining of H157 monolayer cell cultures. (60x image, red: β-catenin, blue: DAPI). Note the dramatic increase in nuclear localization and the decrease in cellular membrane localization of A549 AC, Wnt11-A549 and H157 SCC cell lines compared to the normal pulmonary epithelium (SAEC). Data presented are representative of three independent experiments. <b>E</b>: Densitometry of immunofluorescent images of SAEC, A549, Wnt-11-A549 and H157 cells. Note the increased nuclear localization of β-catenin particularly in the Wnt11-A549 cell line. (M: cellular membrane, CS: cytosol, N: nucleus).</p

Level of Wnt signalling molecules in AC and SCC.

<p>Pooled cDNA of 12 AC, 8 SSC samples were targeted to gene expression analysis using a commercially available Taqman array. Four housekeeping genes were used (18S, GAPDH, HPRT1, GUSB). <b>A:</b> Expression profile of AC. Pooled cDNA of autologous normal tissue samples of the same AC patients served as reference. Note the increased level of the canonical Wnt-7b, and the receptor Fzd-3. (For the list of all gene expression changes see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057393#pone.0057393.s004" target="_blank">Table S1</a>). <b>B:</b> Gene expression levels of SCC. Pooled cDNA of autologous normal tissue samples of the same SCC patients served as reference. Note the upregulation of the non-canonical Wnt5a and the canonical pathway inhibitor Dkk-1, along with increased level of Fzd-10 gene expression. (For the list of all gene expression changes see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057393#pone.0057393.s005" target="_blank">Table S2</a>). <b>C:</b> Gene expression of SCC compared to AC. Note the increased level of non-canonical Wnts (Wnt5a and Wnt11), several receptors (Fzd-7, -9, -10), a canonical pathway inhibitor (Dkk-1) and an inhibitory receptor (Krm2). (For the list of all gene expression changes see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057393#pone.0057393.s006" target="_blank">Table S3</a>). <b>D and E:</b> Immunohistochemical staining of primary control (Panel D) and AC (Panel E) tissues for Wnt11. Note the higher Wnt11 expression in the tumours emphasizing the relative nature of the initially identified differences at mRNA level. Images shown are representatives of three independent stainings. <b>F:</b> Wnt11 gene transcription was also measured in an AC (A549) and an SCC (H157) cancer cell line. Note the higher Wnt11 levels in the observed cancer cell lines compared to the normal, non-cancerous pulmonary epithelium (SAEC). The AC cell line showed a more pronounced increase in Wnt11 expression than the SCC cell line. (The results are representative of three independent experiments where the non-cancerous control (SAEC) was derived from three individual donors of different ages).</p