A point-feature label placement algorithm based on spatial data mining

The point-feature label placement (PFLP) refers to the process of positioning labels near point features on a map while adhering to specific rules and guidelines, finally obtaining clear, aesthetically pleasing, and conflict-free maps. While various approaches have been suggested for automated point feature placement on maps, few studies have fully considered the spatial distribution characteristics and label correlations of point datasets, resulting in poor label quality in the process of solving the label placement of dense and complex point datasets. In this paper, we propose a point-feature label placement algorithm based on spatial data mining that analyzes the local spatial distribution characteristics and label correlations of point features. The algorithm quantifies the interference among point features by designing a label frequent pattern framework (LFPF) and constructs an ascending label ordering method based on the pattern to reduce interference. Besides, three classical metaheuristic algorithms (simulated annealing algorithm, genetic algorithm, and ant colony algorithm) are applied to the PFLP in combination with the framework to verify the validity of this framework. Additionally, a bit-based grid spatial index is proposed to reduce cache memory and consumption time in conflict detection. The performance of the experiments is tested with 4000, 10000, and 20000 points of POI data obtained randomly under various label densities. The results of these experiments showed that: (1) the proposed method outperformed both the original algorithm and recent literature, with label quality improvements ranging from 3 to 6.7 and from 0.1 to 2.6, respectively. (2) The label efficiency was improved by 58.2% compared with the traditional grid index

Assessment of a Novel VEGF Targeted Agent Using Patient-Derived Tumor Tissue Xenograft Models of Colon Carcinoma with Lymphatic and Hepatic Metastases

The lack of appropriate tumor models of primary tumors and corresponding metastases that can reliably predict for response to anticancer agents remains a major deficiency in the clinical practice of cancer therapy. It was the aim of our study to establish patient-derived tumor tissue (PDTT) xenograft models of colon carcinoma with lymphatic and hepatic metastases useful for testing of novel molecularly targeted agents. PDTT of primary colon carcinoma, lymphatic and hepatic metastases were used to create xenograft models. Hematoxylin and eosin staining, immunohistochemical staining, genome-wide gene expression analysis, pyrosequencing, qRT-PCR, and western blotting were used to determine the biological stability of the xenografts during serial transplantation compared with the original tumor tissues. Early passages of the PDTT xenograft models of primary colon carcinoma, lymphatic and hepatic metastases revealed a high degree of similarity with the original clinical tumor samples with regard to histology, immunohistochemistry, genes expression, and mutation status as well as mRNA expression. After we have ascertained that these xenografts models retained similar histopathological features and molecular signatures as the original tumors, drug sensitivities of the xenografts to a novel VEGF targeted agent, FP3 was evaluated. In this study, PDTT xenograft models of colon carcinoma with lymphatic and hepatic metastasis have been successfully established. They provide appropriate models for testing of novel molecularly targeted agents

Transcriptional profiling of differentially expressed long non-coding RNAs in breast cancer

Long non-coding RNAs (lncRNAs) are subclass of noncoding RNAs that have been recently shown to play critical roles in cancer biology. However, little is known about their mechanistic role in breast cancer pathogenesis, especially in triple-negative breast carcinomas (TNBC) that have particular poor outcomes. This study was specifically designed to identify the signatures relevant lncRNAs in breast cancer and characterize lncRNAs that modulate the phenotype. Here we provide detailed methods and analysis of microarray data, which is deposited in the Gene Expression Omnibus (GEO) with the accession number GSE64790. The basic analysis as contained in the manuscript published in Oncotarget with the PMID 26078338. These data can be used to further elucidate the mechanisms of breast cancer

A Stochastic Simulation Model for Monthly River Flow in Dry Season

Streamflow simulation gives the major information on water systems to water resources planning and management. The monthly river flows in dry season often exhibit high autocorrelation. The headwater catchment of the Yellow River basin monthly flow series in dry season exhibits this clearly. However, existing models usually fail to capture the high-dimensional, nonlinear dependence. To address this issue, a stochastic model is developed using canonical vine copulas in combination with nonlinear correlation coefficients. Kendall’s tau values of different pairs of river flows are calculated to measure the mutual correlations so as to select correlated streamflows for every month. Canonical vine copula is used to capture the temporal dependence of every month with its correlated streamflows. Finally, monthly river flow by the conditional joint distribution functions conditioned upon the corresponding river flow records was generated. The model was applied to the simulation of monthly river flows in dry season at Tangnaihai station, which controls the streamflow of headwater catchment of Yellow River basin in the north of China. The results of the proposed method possess a smaller mean absolute error (MAE) than the widely-used seasonal autoregressive integrated moving average model. The performance test on seasonal distribution further verifies the great capacity of the stochastic-statistical method

A Management Method of Multi-Granularity Dimensions for Spatiotemporal Data

To understand the complex phenomena in social space and monitor the dynamic changes in people’s tracks, we need more cross-scale data. However, when we retrieve data, we often ignore the impact of multi-scale, resulting in incomplete results. To solve this problem, we proposed a management method of multi-granularity dimensions for spatiotemporal data. This method systematically described dimension granularity and the fuzzy caused by dimension granularity, and used multi-scale integer coding technology to organize and manage multi-granularity dimensions, and realized the integrity of the data query results according to the correlation between the different scale codes. We simulated the time and band data for the experiment. The experimental results showed that: (1) this method effectively solves the problem of incomplete query results of the intersection query method. (2) Compared with traditional string encoding, the query efficiency of multiscale integer encoding is twice as high. (3) The proportion of different dimension granularity has an impact on the query effect of multi-scale integer coding. When the proportion of fine-grained data is high, the advantage of multi-scale integer coding is greater

The Utility of Sentinel Lymph Node Biopsy in Papillary Thyroid Carcinoma with Occult Lymph Nodes

<div><p>Background</p><p>The sentinel lymph node (SLN) is defined as the first draining node from the primary lesion, and it has proven to be a good indicator of the metastatic status of regional lymph nodes in solid tumors. The aim of this study was to evaluate the clinical application of SLN biopsy (SLNB) in papillary thyroid carcinoma (PTC) with occult lymph nodes.</p><p>Methods</p><p>From April 2006 to October 2012, 212 consecutive PTC patients were treated with SLNB using carbon nanoparticle suspension (CNS). Then, the stained nodes defined as SLN were collected, and prophylactic central compartment neck dissection (CCND) followed by total thyroidectomy or subtotal thyroidectomy were performed. All the samples were sent for pathological examination.</p><p>Results</p><p>There were 78 (36.8%) SLN metastasis (SLNM)-positive cases and 134 (63.2%) SLNM-negative cases. The sensitivity, specificity, positive and negative predictive values, and false-positive and false-negative rates of SLNB were 78.8%, 100%, 100%, 84.3%, 0%, and 21.2%, respectively. The PTC patients with SLNM were more likely to be male (48.2% vs. 32.7%, p = 0.039) and exhibited multifocality (52.6% vs. 33.3%, p = 0.025) and extrathyroidal extension (56.7% vs. 33.5%, p = 0.015). A greater incidence of non-SLN metastases in the central compartment was found in patients with SLNM (41/78, 52.6%) than in those without SLNM (21/134, 15.7%; p < 0.05). However, the SLNM-negative PTC patients with non-SLN metastases were more likely to be male (37.9% vs. 9.5%, p < 0.05).</p><p>Conclusions</p><p>The application of SLNB using CNS is technically feasible, safe, and useful, especially for male patients with co-existing multifocality and extrathyroidal extension. However, the sensitivity of SLNB must be improved and its false-negative rate reduced before it can be a routine procedure and replace prophylactic CCND. More attention should be paid to PTC patients (especially males) without SLNM for signs of non-SLN metastases.</p></div

Multivariate analysis of the clinicopathological factors for patients with SLN metastasis.

<p>SLN = sentinel lymph node;</p><p>* p<0.05</p><p>Multivariate analysis of the clinicopathological factors for patients with SLN metastasis.</p

Overview of SLNB.

<p>SLNB = sentinel lymph node biopsy; SLN = sentinel lymph node; Non-SLN = non-sentinel lymph node.</p

Patient demographics and tumor characteristics (n = 212).

<p>TMAb = thyroid microsomal antibody; TGAb = thyroglobulin antibody; TSH = thyroid stimulating hormone; SLN = sentinel lymph node; Non-SLN = non-sentinel lymph node; Total-LN = total lymph nodes</p><p>Patient demographics and tumor characteristics (n = 212).</p

SLNB using CNS. The white arrow indicates the black-stained SLN in the central compartment.

<p>SLNB = sentinel lymph node biopsy; SLN = sentinel lymph node; CNS = carbon nanoparticle suspension.</p