Latent Space Model for Multi-Modal Social Data

With the emergence of social networking services, researchers enjoy the increasing availability of large-scale heterogenous datasets capturing online user interactions and behaviors. Traditional analysis of techno-social systems data has focused mainly on describing either the dynamics of social interactions, or the attributes and behaviors of the users. However, overwhelming empirical evidence suggests that the two dimensions affect one another, and therefore they should be jointly modeled and analyzed in a multi-modal framework. The benefits of such an approach include the ability to build better predictive models, leveraging social network information as well as user behavioral signals. To this purpose, here we propose the Constrained Latent Space Model (CLSM), a generalized framework that combines Mixed Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA) incorporating a constraint that forces the latent space to concurrently describe the multiple data modalities. We derive an efficient inference algorithm based on Variational Expectation Maximization that has a computational cost linear in the size of the network, thus making it feasible to analyze massive social datasets. We validate the proposed framework on two problems: prediction of social interactions from user attributes and behaviors, and behavior prediction exploiting network information. We perform experiments with a variety of multi-modal social systems, spanning location-based social networks (Gowalla), social media services (Instagram, Orkut), e-commerce and review sites (Amazon, Ciao), and finally citation networks (Cora). The results indicate significant improvement in prediction accuracy over state of the art methods, and demonstrate the flexibility of the proposed approach for addressing a variety of different learning problems commonly occurring with multi-modal social data.Comment: 12 pages, 7 figures, 2 table

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Intensifying chitin hydrolysis by adjunct treatments – an overview

Chitin is, after cellulose, the most abundant organic natural polysaccharide on Earth, being synthesized as a dominant component in the exoskeletons of crustaceans, among other sources. In the processing of seafood for human consumption, between 40 and 50% of the total raw material mass is wasted, causing a significant problem for the environment due to its slow degradation. Efforts to find uses for chitin derivatives, particularly their oligomers, have intensified since these chemicals are highly functional and offer a wide range of applications, especially as antimicrobial agent. As a consequence, some adjunct treatments, either chemical or physical in nature, have been employed to assist acid and enzymatic hydrolysis. This work provides a detailed review of the methods employed to intensify the formation of chitin oligomers, particularly focusing on the adjunct treatments used (microwave, ultrasonication, steam explosion and gamma irradiation), and evaluate the yield and characteristics of the oligomers formed. Adjunct treatments are more suitable for enzymatic hydrolysis since these treatments modify the chitin structure, and enhance the hydrolysis rate and yield of the oligomers, under milder reaction conditions. For future research, it would be worth trying pre-treatments like the application of high-pressure to chitin in order to lower its crystallinity

Development and evaluation of nanoemulsion and microsuspension formulations of curcuminoids for lung delivery with a novel approach to understanding the aerosol performance of nanoparticles

YesExtensive research has demonstrated the potential effectiveness of curcumin against various diseases, including asthma and cancers. However, few studies have used liquid-based vehicles in the preparation of curcumin formulations. Therefore, the current study proposed the use of nanoemulsion and microsuspension formulations to prepare nebulised curcuminoid for lung delivery. Furthermore, this work expressed a new approach to understanding the aerosol performance of nanoparticles compared to microsuspension formulations. The genotoxicity of the formulations was also assessed. Curcuminoid nanoemulsion formulations were prepared in three concentrations (100, 250 and 500 µg/ml) using limonene and oleic acid as oil phases, while microsuspension solutions were prepared by suspending curcuminoid particles in isotonic solution (saline solution) of 0.02% Tween 80. The average fine particle fraction (FPF) and mass median aerodynamic diameter (MMAD) of the nebulised microsuspension formulations ranged from 26% and 7.1 µm to 40% and 5.7 µm, for 1000 µg/ml and 100 µg/ml respectively. In a comparison of the low and high drug concentrations of the nebulised nanoemulsion, the average FPF and MMAD of the nebulised nanoemulsion formulations prepared with limonene oil ranged from 50% and 4.6 µm to 45% and 5.6 µm, respectively; whereas the FPF and MMAD of the nebulised nanoemulsion prepared with oleic acid oil ranged from 46% and 4.9 µm to 44% and 5.6 µm, respectively. The aerosol performance of the microsuspension formulations were concentration dependent, while the nanoemulsion formulations did not appear to be dependent on the curcuminoids concentration. The performance and genotoxicity results of the formulations suggest the suitability of these preparations for further inhalation studies in animals

Properties and customization of sensor materials for biomedical applications.

Low-power chemo- and biosensing devices capable of monitoring clinically important parameters in real time represent a great challenge in the analytical field as the issue of sensor calibration pertaining to keeping the response within an accurate calibration domain is particularly significant (1–4). Diagnostics, personal health, and related costs will also benefit from the introduction of sensors technology (5–7). In addition, with the introduction of Registration, Evaluation, Authorization, and Restriction of Chemical Substances (REACH) regulation, unraveling the cause–effect relationships in epidemiology studies will be of outmost importance to help establish reliable environmental policies aimed at protecting the health of individuals and communities (8–10). For instance, the effect of low concentration of toxic elements is seldom investigated as physicians do not have means to access the data (11)

Influence of henna extracts on static and dynamic adsorption of sodium dodecyl sulfate and residual oil recovery from quartz sand

The application of surfactant flooding for enhanced oil recovery (EOR) promotes hydrocarbon recovery through reduction of oil-water interfacial tension and alteration of oil-wet rock wettability into the water-wet state. Unfortunately, surfactant depletion in porous media, due to surfactant molecule adsorption and retention, adversely affects oil recovery, thus increasing the cost of the surfactant flooding process. Chemical-based materials are normally used as inhibitors or sacrificial agents to minimize surfactant adsorption, but they are quite expensive and not environmentally friendly. Plant-based materials (henna extracts) are far more sustainable because they are obtained from natural sources. However, there is limited research on the application of henna extracts as inhibitors to reduce dynamic adsorption of the surfactant in porous media and improve oil recovery from such media. Thus, henna extracts were introduced as an eco-friendly and low-cost sacrificial agent for minimizing the static and dynamic adsorption of sodium dodecyl sulfate (SDS) onto quartz sand in this study. Results showed that the extent of surfactant adsorption was inversely proportional to the henna extract concentration, and the adsorption of the henna extract onto the quartz surface was a multilayer adsorption that followed the Freundlich isotherm model. Precisely, the henna extract adsorption on quartz sand is in the range of 3.12-4.48 mg/g (for static adsorption) and 5.49-6.73 mg/g (for dynamic adsorption), whereas the SDS adsorption on quartz sand was obtained as 2.11 and 4.79 mg/g at static and dynamic conditions, respectively. In the presence of 8000 mg/L henna extract, SDS static and dynamic adsorption was significantly reduced by 64 and 82%, respectively. At the same conditions, the residual oil recovery increased by 9.2% over normal surfactant flooding. The study suggests that the use of henna extracts as a sacrificial