Desktop Content Manager

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2004.Includes bibliographical references (p. 65-66).Researchers and students gather a wealth of information from the Internet. With increasingly affordable large hard disk spaces, much content can be archived in the computer for future reference. However, the only means for organizing this information on today's machines is the folder system. Information on a topic may be spread over many folders. This provides for poor content navigation and retrieval. This thesis describes how the concept of keyword-based indexing can be applied towards navigation of electronic content. The idea is implemented in the form of a software solution - Desktop Content Manager. The information on keywords and content are stored and represented in a manner facilitating automatic hyper linking of related information providing immediate access. The software was tested with sample content extracted from different sources and the scalability and limitations of the suggested approach identified. The findings are used to define further work in this area.by Deepak Ravichandran.S.M

Methods and Systems to Identify Potential Users Using Click-Based Referral of Website Visitors

This document describes systems and processes for an advertising platform. The advertising platform increases a size of a target audience (e.g., advertising list of an advertising campaign) of an advertiser by using information that is known about the visitors to the advertiser’s website. The advertising platform can enable advertisers (e.g., website administrators or other such online content providers) to expand advertising to web users that have similar interests to users that have visited the website (e.g., visitors, customers, etc.) of the advertiser. These similar users are more likely to purchase a product from the advertiser than a typical web user. The advertising platform uses click data to characterize users of an initial user list. The initial user list can be small when compared to the online marketplace as a whole. The advertising platform uses the click data of users of the initial user list (e.g., a seed list) to represent the users of the initial user list. The advertising platform uses the click data refer additional web users to the list and generate a target list. Each user of the initial user list is limited to a maximum number of referrals to the target list to maintain diversity in the type of user on the target list, such that no user’s click data model dominates the target list

Single-cell analysis of cardiogenesis reveals basis for organ-level developmental defects.

Organogenesis involves integration of diverse cell types; dysregulation of cell-type-specific gene networks results in birth defects, which affect 5% of live births. Congenital heart defects are the most common malformations, and result from disruption of discrete subsets of cardiac progenitor cells1, but the transcriptional changes in individual progenitors that lead to organ-level defects remain unknown. Here we used single-cell RNA sequencing to interrogate early cardiac progenitor cells as they become specified during normal and abnormal cardiogenesis, revealing how dysregulation of specific cellular subpopulations has catastrophic consequences. A network-based computational method for single-cell RNA-sequencing analysis that predicts lineage-specifying transcription factors2,3 identified Hand2 as a specifier of outflow tract cells but not right ventricular cells, despite the failure of right ventricular formation in Hand2-null mice4. Temporal single-cell-transcriptome analysis of Hand2-null embryos revealed failure of outflow tract myocardium specification, whereas right ventricular myocardium was specified but failed to properly differentiate and migrate. Loss of Hand2 also led to dysregulation of retinoic acid signalling and disruption of anterior-posterior patterning of cardiac progenitors. This work reveals transcriptional determinants that specify fate and differentiation in individual cardiac progenitor cells, and exposes mechanisms of disrupted cardiac development at single-cell resolution, providing a framework for investigating congenital heart defects

Enhanced EPR directed and Imaging guided Photothermal Therapy using Vitamin E Modified Toco-Photoxil

Herein we report synthesis, characterization and preclinical applications of a novel hybrid nanomaterial Toco-Photoxil developed using vitamin E modified gold coated poly (lactic-co-glycolic acid) nanoshells incorporating Pgp inhibitor d-α-tocopheryl polyethylene glycol 1000 succinate (TPGS) as a highly inert and disintegrable photothermal therapy (PTT) agent. Toco-Photoxil is highly biocompatible, physiologically stable PTT material with an average diameter of 130 nm that shows good passive accumulation (2.3% ID) in solid tumors when delivered systemically. In comparison to its surface modified counterparts such as IR780-Toco-Photoxil, FA-Toco-Photoxil or FA-IR780-Toco-Photoxil accumulation are merely ~0.3% ID, ~0.025% ID and ~0.005% ID in folate receptor (FR) negative and positive tumor model. Further, Toco-Photoxil variants are prepared by tuning the material absorbance either at 750 nm (narrow) or 915 nm (broad) to study optimal therapeutic efficacy in terms of peak broadness and nanomaterial’s concentration. Our findings suggest that Toco-Photoxil tuned at 750 nm absorbance is more efficient (P = 0.0097) in preclinical setting. Toco-Photoxil shows complete passiveness in critical biocompatibility test and reasonable body clearance. High tumor specific accumulation from systemic circulation, strong photothermal conversion and a very safe material property in body physiology makes Toco-Photoxil a superior and powerful PTT agent, which may pave its way for fast track clinical trial in future

Discovering gene annotations in biomedical text databases

<p>Abstract</p> <p>Background</p> <p>Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data.</p> <p>Results</p> <p>In this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products.</p> <p>In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general.</p> <p>Conclusion</p> <p>GEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values.</p

The impact of immediate breast reconstruction on the time to delivery of adjuvant therapy: the iBRA-2 study

Background: Immediate breast reconstruction (IBR) is routinely offered to improve quality-of-life for women requiring mastectomy, but there are concerns that more complex surgery may delay adjuvant oncological treatments and compromise long-term outcomes. High-quality evidence is lacking. The iBRA-2 study aimed to investigate the impact of IBR on time to adjuvant therapy. Methods: Consecutive women undergoing mastectomy ± IBR for breast cancer July–December, 2016 were included. Patient demographics, operative, oncological and complication data were collected. Time from last definitive cancer surgery to first adjuvant treatment for patients undergoing mastectomy ± IBR were compared and risk factors associated with delays explored. Results: A total of 2540 patients were recruited from 76 centres; 1008 (39.7%) underwent IBR (implant-only [n = 675, 26.6%]; pedicled flaps [n = 105,4.1%] and free-flaps [n = 228, 8.9%]). Complications requiring re-admission or re-operation were significantly more common in patients undergoing IBR than those receiving mastectomy. Adjuvant chemotherapy or radiotherapy was required by 1235 (48.6%) patients. No clinically significant differences were seen in time to adjuvant therapy between patient groups but major complications irrespective of surgery received were significantly associated with treatment delays. Conclusions: IBR does not result in clinically significant delays to adjuvant therapy, but post-operative complications are associated with treatment delays. Strategies to minimise complications, including careful patient selection, are required to improve outcomes for patients

Learning Surface Text Patterns for a Question Answering System

In this paper we explore the power of surface text patterns for open-domain question answering systems. In order to obtain an optimal set of patterns, we have developed a method for learning such patterns automatically. A tagged corpus is built from the Internet in a bootstrapping process by providing a few hand-crafted examples of each question type to Altavista. Patterns are then automatically extracted from the returned documents and standardized. We calculate the precision of each pattern, and the average precision for each question type. These patterns are then applied to find answers to new questions