Real time predictive monitoring system for urban transport

Ubiquitous access to mobile and internet technology has influenced a significant increase in the amount of data produced, communicated and stored by corporations as well as by individual users, in recent years. The research presented in this thesis proposes an architectural framework to acquire, store, manipulate and integrate data and information within an urban transport environment, to optimise its operations in real-time. The deployed architecture is based on the integration of a number of technologies and tailor-made algorithms implemented to provide a management tool to aid traffic monitoring, using intelligent decision-making processes. A creative combination of Data Mining techniques and Machine Learning algorithms was used to implement predictive analytics, as a key component in the process of addressing challenges in monitoring and managing an urban transport network operation in real-time. The proposed solution has then been applied to an actual urban transport management system, within a partner company, Mermaid Technology, Copenhagen to test and evaluate the proposed algorithms and the architectural integration principles used. Various visualization methods have been employed, at numerous stages of the project to dynamically interpret the large volume and diversity of data to effectively aid the monitoring and decision-making process. The deliverables on this project include: the system architecture design, as well as software solutions, which facilitate predictive analytics and effective visualisation strategies to aid real-time monitoring of a large system, in the context of urban transport. The proposed solutions have been implemented, tested and evaluated in a Case Study in collaboration with Mermaid Technology. Using live data from their network operations, it has aided in evaluating the efficiency of the proposed system

Genomic Signatures Predict Poor Outcome in Undifferentiated Pleomorphic Sarcomas and Leiomyosarcomas

<div><p>Undifferentiated high-grade pleomorphic sarcomas (UPSs) display aggressive clinical behavior and frequently develop local recurrence and distant metastasis. Because these sarcomas often share similar morphological patterns with other tumors, particularly leiomyosarcomas (LMSs), classification by exclusion is frequently used. In this study, array-based comparative genomic hybridization (array CGH) was used to analyze 20 UPS and 17 LMS samples from untreated patients. The LMS samples presented a lower frequency of genomic alterations compared with the UPS samples. The most frequently altered UPS regions involved gains at 20q13.33 and 7q22.1 and losses at 3p26.3. Gains at 8q24.3 and 19q13.12 and losses at 9p21.3 were frequently detected in the LMS samples. Of these regions, gains at 1q21.3, 11q12.2-q12.3, 16p11.2, and 19q13.12 were significantly associated with reduced overall survival times in LMS patients. A multivariate analysis revealed that gains at 1q21.3 were an independent prognostic marker of shorter survival times in LMS patients (HR = 13.76; <i>P</i> = 0.019). Although the copy number profiles of the UPS and LMS samples could not be distinguished using unsupervised hierarchical clustering analysis, one of the three clusters presented cases associated with poor prognostic outcome (<i>P = </i>0.022). A relative copy number analysis for the <i>ARNT</i>, <i>SLC27A3,</i> and <i>PBXIP1</i> genes was performed using quantitative real-time PCR in 11 LMS and 16 UPS samples. Gains at 1q21-q22 were observed in both tumor types, particularly in the UPS samples. These findings provide strong evidence for the existence of a genomic signature to predict poor outcome in a subset of UPS and LMS patients.</p></div

Genomic imbalances more frequently detected in UPS and LMS.

<p>Legend: UPS - Undifferentiated Pleomorphic Sarcomas; LMS - Leiomyosarcomas.</p

Clinical and histopathological data from patients (20 UPS and 17 LMS).

<p>Abbreviations - F: Female, M: Male, DD: Death by disease, NED: No evidence of disease, AD: Alive with disease, LF: Loss of follow-up; MD: metastasis at diagnosis.</p><p>Treatment – QT: Chemotherapy; RT: Radiotherapy; 0: Surgery; 1: Neoadjuvant therapy; 2: Adjuvant therapy; 3: Chemotherapy without surgery.</p>*<p>Selected for qPCR validation.</p>a<p>Samples from the same patient.</p>b<p>Patients with Li-Fraumeni Syndrome.</p>c<p>Time to last follow-up from diagnosis.</p>d<p>Samples of different patients obtained from expansion of primary tumor surgical (remnant of primary tumor).</p

Quantification of DNA copy number alterations using qPCR for the <i>ARNT</i>, <i>PBXIP1, SLC27A3,</i> and <i>CCND1</i> genes.

<p>Eight primer pairs were designed, including (A) three for <i>ARNT</i> (ARNT-P1, ARNT-P2, and ARNT-P3); (B) two for <i>PBXIP1</i> (PBXIP1-P1 and PBXIP1-P2) and one for <i>SLC27A3</i> (SLC27A3-P1); and (C) two for <i>CCND1</i> (CCND1-P1 and CCND1-P2).</p

Unsupervised hierarchical clustering of 20 undifferentiated pleomorphic sarcomas (UPSs) and 17 leiomyosarcomas (LMSs).

<p>(A) In the dendrogram, cluster 1 is shown in green, cluster 2 is shown in blue, and cluster 3 is shown in red. Clusters related to the sites of anatomical origin were not observed for these tumors; origin sites include the following regions: upper extremity (pink), lower extremity (purple), trunk (orange), retroperitoneum (yellow), and head and neck (rose). (B) Genomic alterations were detected in clusters 1 (C1; 11 cases), 2 (C2; 16 cases), and 3 (C3; 10 cases). The top bars (blue) indicate genetic gains, whereas the lower bars (red) indicate genetic losses. The images shown were adapted from the output of the Nexus 6.0 software program.</p

Overall survival curves from LMS patients with specific genomic alterations.

<p>Gains at (A) 1q21.3, (B) 11q12.2-q12.3, (C) 16p11.2, and (D) 19q13.12 were associated with shorter survival times. P-values were determined using the Log-rank test.</p

Gene Expression Profiling in Leiomyosarcomas and Undifferentiated Pleomorphic Sarcomas: SRC as a New Diagnostic Marker

<div><p>Background</p><p>Undifferentiated Pleomorphic Sarcoma (UPS) and high-grade Leiomyosarcoma (LMS) are soft tissue tumors with an aggressive clinical behavior, frequently developing local recurrence and distant metastases. Despite several gene expression studies involving soft tissue sarcomas, the potential to identify molecular markers has been limited, mostly due to small sample size, in-group heterogeneity and absence of detailed clinical data.</p><p>Materials and Methods</p><p>Gene expression profiling was performed for 22 LMS and 22 UPS obtained from untreated patients. To assess the relevance of the gene signature, a meta-analysis was performed using five published studies. Four genes (<i>BAD</i>, <i>MYOCD</i>, <i>SRF</i> and <i>SRC</i>) selected from the gene signature, meta-analysis and functional <i>in silico</i> analysis were further validated by quantitative PCR. In addition, protein-protein interaction analysis was applied to validate the data. SRC protein immunolabeling was assessed in 38 UPS and 52 LMS.</p><p>Results</p><p>We identified 587 differentially expressed genes between LMS and UPS, of which 193 corroborated with other studies. Cluster analysis of the data failed to discriminate LMS from UPS, although it did reveal a distinct molecular profile for retroperitoneal LMS, which was characterized by the over-expression of smooth muscle-specific genes. Significantly higher levels of expression for <i>BAD</i>, <i>SRC</i>, <i>SRF</i>, and <i>MYOCD</i> were confirmed in LMS when compared with UPS. <i>SRC</i> was the most value discriminator to distinguish both sarcomas and presented the highest number of interaction in the <i>in silico</i> protein-protein analysis. SRC protein labeling showed high specificity and a positive predictive value therefore making it a candidate for use as a diagnostic marker in LMS.</p><p>Conclusions</p><p>Retroperitoneal LMS presented a unique gene signature. SRC is a putative diagnostic marker to differentiate LMS from UPS.</p></div

SRC protein expression levels evaluated by immunohistochemistry in 52 LMS and 38 UPS.

<p>UPS: undifferentiated pleomorphic sarcoma; LMS: leiomyosarcoma; UPS-R: retroperitoneal UPS; UPS-NR: non-retroperitoneal UPS (including head and neck, trunk and extremity); LMS-R: retroperitoneal LMS; LMS-NR: non-retroperitoneal LMS (including head and neck, trunk and extremity);</p><p>*Chi-square test;</p>#<p>Fisher exact test.</p

Protein-protein interaction network of 59 genes derived from the meta-analysis.

<p>Only proteins with more than 10 interactions partners were considered. The triangle size is proportional to the number of interactions for each protein. SRC presented the largest number of interactions, including four strong physical interactions found in the network. The interaction partners for each protein were obtained from the Interologous Interaction Database (I2D) 2.0 and the network was visualized and analyzed with the NAViGaTOR 2.3 software.</p