Temporal Analysis on Topics Using Word2Vec

The present study proposes a novel method of trend detection and visualization - more specifically, modeling the change in a topic over time. Where current models used for the identification and visualization of trends only convey the popularity of a singular word based on stochastic counting of usage, the approach in the present study illustrates the popularity and direction that a topic is moving in. The direction in this case is a distinct subtopic within the selected corpus. Such trends are generated by modeling the movement of a topic by using k-means clustering and cosine similarity to group the distances between clusters over time. In a convergent scenario, it can be inferred that the topics as a whole are meshing (tokens between topics, becoming interchangeable). On the contrary, a divergent scenario would imply that each topics' respective tokens would not be found in the same context (the words are increasingly different to each other). The methodology was tested on a group of articles from various media houses present in the 20 Newsgroups dataset

Single nucleotide polymorphism mining and nucleotide sequence analysis of Mx1 gene in exonic regions of Japanese quail

Aim: An attempt has been made to study the Myxovirus resistant (Mx1) gene polymorphism in Japanese quail. Materials and Methods: In the present, investigation four fragments viz. Fragment I of 185 bp (Exon 3 region), Fragment II of 148 bp (Exon 5 region), Fragment III of 161 bp (Exon 7 region), and Fragment IV of 176 bp (Exon 13 region) of Mx1 gene were amplified and screened for polymorphism by polymerase chain reaction-single-strand conformation polymorphism technique in 170 Japanese quail birds. Results: Out of the four fragments, one fragment (Fragment II) was found to be polymorphic. Remaining three fragments (Fragment I, III, and IV) were found to be monomorphic which was confirmed by custom sequencing. Overall nucleotide sequence analysis of Mx1 gene of Japanese quail showed 100% homology with common quail and more than 80% homology with reported sequence of chicken breeds. Conclusion: The Mx1 gene is mostly conserved in Japanese quail. There is an urgent need of comprehensive analysis of other regions of Mx1 gene along with its possible association with the traits of economic importance in Japanese quail

Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT

<div><p>Radiomics aims to quantitatively capture the complex tumor phenotype contained in medical images to associate them with clinical outcomes. This study investigates the impact of different types of computed tomography (CT) images on the prognostic performance of radiomic features for disease recurrence in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT). 112 early stage NSCLC patients treated with SBRT that had static free breathing (FB) and average intensity projection (AIP) images were analyzed. Nineteen radiomic features were selected from each image type (FB or AIP) for analysis based on stability and variance. The selected FB and AIP radiomic feature sets had 6 common radiomic features between both image types and 13 unique features. The prognostic performances of the features for distant metastasis (DM) and locoregional recurrence (LRR) were evaluated using the concordance index (CI) and compared with two conventional features (tumor volume and maximum diameter). P-values were corrected for multiple testing using the false discovery rate procedure. None of the FB radiomic features were associated with DM, however, seven AIP radiomic features, that described tumor shape and heterogeneity, were (CI range: 0.638–0.676). Conventional features from FB images were not associated with DM, however, AIP conventional features were (CI range: 0.643–0.658). Radiomic and conventional multivariate models were compared between FB and AIP images using cross validation. The differences between the models were assessed using a permutation test. AIP radiomic multivariate models (median CI = 0.667) outperformed all other models (median CI range: 0.601–0.630) in predicting DM. None of the imaging features were prognostic of LRR. Therefore, image type impacts the performance of radiomic models in their association with disease recurrence. AIP images contained more information than FB images that were associated with disease recurrence in early stage NSCLC patients treated with SBRT, which suggests that AIP images may potentially be more optimal for the development of an imaging biomarker.</p></div

Performance of each multivariate model in predicting distant metastasis.

<p>Concordance indices are reported for the FB and AIP conventional and radiomic models, and a combined FB+AIP radiomics model, comparing the performance of each of model and image type. Cross validation was performed (80% training, 20% validation) to generate 100 models for each model type. Comb. Indicates the combined FB and AIP radiomics model. *p-value < 0.05; “ns” indicates not significant (p-value > 0.05).</p

Prognostic performance of imaging features derived from A) FB or B) AIP images for disease recurrence in NSCLC patients treated with SBRT.

<p>Concordance indices (CI) are shown for each imaging feature and the clinical outcomes considered (distant metastasis (DM, left) and locoregional recurrence (LRR, right)). “Inv. Prop.”, “Rand.” and “Prop.” indicate inversely proportional, equivalent to a random guess, and directly proportional, respectively. Conventional features are shown in grey and radiomic features are shown in red (shape), blue (statistics), and green (texture). *p-value < 0.05 (Noether’s test, FDR corrected p-values).</p

Patient, tumor, and treatment characteristics and clinical outcomes.

<p>Patient, tumor, and treatment characteristics and clinical outcomes.</p

Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT - Fig 1

<p>A) Examples of free breathing (FB) and average intensity projection (AIP) images, demonstrating the observable differences in tumor phenotype between each image type. AIP images were reconstructed from 4D computed tomography (CT) scans. B) Schematic representation of the radiomics workflow for FB and AIP images. I. CT images of the patient are acquired and the tumor is segmented. II. Imaging features (radiomic and conventional features) are extracted from the tumor volume. III. Radiomic features undergo a feature dimension reduction process to generate a low-dimensional feature set based on feature stability and variance. IV. Imaging features are then analyzed with clinical outcomes to evaluate their prognostic power. FB and AIP radiomics features are compared.</p

Imaging features selected for analysis.

<p>Imaging features selected for analysis.</p