A Hybrid Approach Towards Content Boosted Recommender System

With the exponential increase in data over the web the users face the problem in retrieving relevant knowledge. For eliminating this problem recommenders are used. They are based on one of the traditional recommendation approaches – content based approach and collaborative based approach. Recommendation can be provided to users using past user activities with help of data mining concepts and the market trend can be merged with it to provide optimized results from recommender. The user profile similarity for personalization, the hit based approach for new movies, history based approach all tackle one problem or the other faced by the traditional recommender systems. The paper proposes a new hybrid approach which combines the effect and positive functionality of all the above methods and tries to tackle major problems faced by recommender systems. The approach can be used to develop web based applications in other domains as well. The approach can be further refined by considering additional parameters based on the system’s need. DOI: 10.17762/ijritcc2321-8169.15050

Microemulsions as Nanotemplates: A Soft and Versatile Approach

Template efficacy of microemulsions in generating nanoparticles has garnered considerable attention in the world of colloidal science. A microemulsion is an optically isotropic and thermodynamically stable colloidal dispersion, which possess spherical droplets (either of W/O or O/W) of the size <50 nm. In microemulsions, the spontaneous formation of domains of nanometric dimensions significantly facilitates their exploitation as potential nanoreactors for the production of stable nanoparticles (due to their cost-effectiveness and ease of preparation). The present chapter provides an overview of microemulsions as efficient nanotemplates, with a detailed account of plausible nanomaterials, i.e., metallic nanoparticles, quantum dots, polymeric nanoparticles, mesoporous silica nanoparticles, solid lipid nanoparticles, nanostructured lipid carriers, etc. Based on the high surface area, good crystallinity, controllable particle size, outstanding catalytic, and magnetic properties, the exploitation of nanoparticles as efficient catalysts and drug delivery modules has also been highlighted

Cracking the “Sepsis” Code: Assessing Time Series Nature of EHR data, and Using Deep Learning for Early Sepsis Prediction

On a yearly basis, sepsis costs US hospitals more than any other health condition. A majority of patients who suffer from sepsis are not diagnosed at the time of admission. Early detection and antibiotic treatment of sepsis are vital to improve outcomes for these patients, as each hour of delayed treatment is associated with increased mortality. In this study our goal is to predict sepsis 12 hours before its diagnosis using vitals and blood tests routinely taken in the ICU. We have investigated the performance of several machine learning algorithms including XGBoost, CNN, CNN-LSTM and CNN-XGBoost. Contrary to our expectations, XGBoost outperforms all of the sequential models and yields the best hour-by-hour prediction, perhaps due to the way we imputed missing values, losing signal that relates to the time-series nature of the EHR data. We added feature engineering to detect change points in tests and vitals, resulting in 5% improvement in XGBoost. Our team, USF-Sepsis-Phys, achieved a utility score of 0.22 (untuned threshold) and an average of the three reported AUCs (test sets A, B, C) of 0.82. As expected with this AUC, the same model with tuned threshold (not run in the PhysioNet challenge) performed significantly better, as evaluated with 3-fold cross-validation of the entire PhyisoNet training set

Validation of endogenous reference genes for qRT-PCR analysis of human visceral adipose samples

<p>Abstract</p> <p>Background</p> <p>Given the epidemic proportions of obesity worldwide and the concurrent prevalence of metabolic syndrome, there is an urgent need for better understanding the underlying mechanisms of metabolic syndrome, in particular, the gene expression differences which may participate in obesity, insulin resistance and the associated series of chronic liver conditions. Real-time PCR (qRT-PCR) is the standard method for studying changes in relative gene expression in different tissues and experimental conditions. However, variations in amount of starting material, enzymatic efficiency and presence of inhibitors can lead to quantification errors. Hence the need for accurate data normalization is vital. Among several known strategies for data normalization, the use of reference genes as an internal control is the most common approach. Recent studies have shown that both obesity and presence of insulin resistance influence an expression of commonly used reference genes in omental fat. In this study we validated candidate reference genes suitable for qRT-PCR profiling experiments using visceral adipose samples from obese and lean individuals.</p> <p>Results</p> <p>Cross-validation of expression stability of eight selected reference genes using three popular algorithms, <it>GeNorm</it>, <it>NormFinder </it>and <it>BestKeeper </it>found <it>ACTB </it>and <it>RPII </it>as most stable reference genes.</p> <p>Conclusions</p> <p>We recommend <it>ACTB </it>and <it>RPII </it>as stable reference genes most suitable for gene expression studies of human visceral adipose tissue. The use of these genes as a reference pair may further enhance the robustness of qRT-PCR in this model system.</p

α1A-Adrenergic Receptor Induces Activation of Extracellular Signal-Regulated Kinase 1/2 through Endocytic Pathway

G protein-coupled receptors (GPCRs) activate mitogen-activated protein kinases through a number of distinct pathways in cells. Increasing evidence has suggested that endosomal signaling has an important role in receptor signal transduction. Here we investigated the involvement of endocytosis in α1A-adrenergic receptor (α1A-AR)-induced activation of extracellular signal-regulated kinase 1/2 (ERK1/2). Agonist-mediated endocytic traffic of α1A-AR was assessed by real-time imaging of living, stably transfected human embryonic kidney 293A cells (HEK-293A). α1A-AR was internalized dynamically in cells with agonist stimulation, and actin filaments regulated the initial trafficking of α1A-AR. α1A-AR-induced activation of ERK1/2 but not p38 MAPK was sensitive to disruption of endocytosis, as demonstrated by 4°C chilling, dynamin mutation and treatment with cytochalasin D (actin depolymerizing agent). Activation of protein kinase C (PKC) and C-Raf by α1A-AR was not affected by 4°C chilling or cytochalasin D treatment. U73122 (a phospholipase C [PLC] inhibitor) and Ro 31–8220 (a PKC inhibitor) inhibited α1B-AR- but not α1A-AR-induced ERK1/2 activation. These data suggest that the endocytic pathway is involved in α1A-AR-induced ERK1/2 activation, which is independent of Gq/PLC/PKC signaling

Walk a mile in my shoes

At some point you’re likely to teach a student with a chronic illness or a student who goes home to a family member with a chronic illness. Now there’s a new online health resource to help you help that student

Perception of general dental practitioners toward periodontal treatment: A survey

Periodontology is a fast evolving field where newer insights into existing concepts are changing the face of the traditional periodontal treatment. Constant research is taking place so as to develop a number of newer avenues in the treatment of the periodontal diseases. However, the protocol of management of periodontal diseases in the setup of general dental practice has undergone little change over the last decade. So, a survey was carried out among 100 general dental practitioners by means of a questionnaire to identify the current status of periodontal treatment in dental clinics, the protocol of maintenance therapy, and the general awareness of the dental profession toward periodontal care