High Dimensional Data Enrichment: Interpretable, Fast, and Data-Efficient

High dimensional structured data enriched model describes groups of observations by shared and per-group individual parameters, each with its own structure such as sparsity or group sparsity. In this paper, we consider the general form of data enrichment where data comes in a fixed but arbitrary number of groups G. Any convex function, e.g., norms, can characterize the structure of both shared and individual parameters. We propose an estimator for high dimensional data enriched model and provide conditions under which it consistently estimates both shared and individual parameters. We also delineate sample complexity of the estimator and present high probability non-asymptotic bound on estimation error of all parameters. Interestingly the sample complexity of our estimator translates to conditions on both per-group sample sizes and the total number of samples. We propose an iterative estimation algorithm with linear convergence rate and supplement our theoretical analysis with synthetic and real experimental results. Particularly, we show the predictive power of data-enriched model along with its interpretable results in anticancer drug sensitivity analysis

From Questions to Effective Answers: On the Utility of Knowledge-Driven Querying Systems for Life Sciences Data

We compare two distinct approaches for querying data in the context of the life sciences. The first approach utilizes conventional databases to store the data and intuitive form-based interfaces to facilitate easy querying of the data. These interfaces could be seen as implementing a set of "pre-canned" queries commonly used by the life science researchers that we study. The second approach is based on semantic Web technologies and is knowledge (model) driven. It utilizes a large OWL ontology and same datasets as before but associated as RDF instances of the ontology concepts. An intuitive interface is provided that allows the formulation of RDF triples-based queries. Both these approaches are being used in parallel by a team of cell biologists in their daily research activities, with the objective of gradually replacing the conventional approach with the knowledge-driven one. This provides us with a valuable opportunity to compare and qualitatively evaluate the two approaches. We describe several benefits of the knowledge-driven approach in comparison to the traditional way of accessing data, and highlight a few limitations as well. We believe that our analysis not only explicitly highlights the specific benefits and limitations of semantic Web technologies in our context but also contributes toward effective ways of translating a question in a researcher's mind into precise computational queries with the intent of obtaining effective answers from the data. While researchers often assume the benefits of semantic Web technologies, we explicitly illustrate these in practice

Poly(ethylene glycol)-poly(ε-caprolactone)-based micelles for solubilization and tumor-targeted delivery of silibinin

Introduction: Silibinin is a naturally occurring compound with known positive impacts on prevention and treatment of many types of human illnesses in general and cancer in particular. Silibinin is poorly water soluble which results in its insufficient bioavailability and lack of therapeutic efficacy in cancer. Here, we proposed to examine the potential of micelles composed of poly(ethylene glycol) (PEG) as the hydrophilic block and poly(ε-caprolactone) (PCL), poly(α-benzylcarboxylate-ε-caprolactone) (PBCL), or poly(lactide)-(PBCL) (PLA-PBCL) as hydrophobic blocks for enhancing the water solubility of silibinin and its targeted delivery to tumor. Methods: Co-solvent evaporation method was used to incorporate silibinin into PEG-PCL based micelles. Drug release profiles were assessed using dialysis bag method. MTT assay also was used to analyze functional activity of drug delivery in B16 melanoma cells. Results: Silibinin encapsulated micelles were shown to be less than 60 nm in size. Among different structures under study, the one with PEG-PBCL could incorporate silibinin with the highest encapsulation efficiency being 95.5%, on average. PEG-PBCL micelles could solubilize 1 mg silibinin in 1 mL water while the soluble amount of silibinin was found to be 0.092 mg/mL in the absence of polymeric micelles. PEG-PBCL micelles provided the sustained release of silibinin indicated with less than 30% release of silibinin within 24 hours. Silibinin encapsulated in PEG-PBCL micelles resulted in growth inhibitory effect in B16 cancer cells which was significantly higher than what observed with free drug. Conclusion: Our findings showed that PEG-PBCL micellar nanocarriers can be a useful vehicle for solubilization and targeted delivery of silibinin

A unified framework for managing provenance information in translational research

<p>Abstract</p> <p>Background</p> <p>A critical aspect of the NIH <it>Translational Research </it>roadmap, which seeks to accelerate the delivery of "bench-side" discoveries to patient's "bedside," is the management of the <it>provenance </it>metadata that keeps track of the origin and history of data resources as they traverse the path from the bench to the bedside and back. A comprehensive provenance framework is essential for researchers to verify the quality of data, reproduce scientific results published in peer-reviewed literature, validate scientific process, and associate trust value with data and results. Traditional approaches to provenance management have focused on only partial sections of the translational research life cycle and they do not incorporate "domain semantics", which is essential to support domain-specific querying and analysis by scientists.</p> <p>Results</p> <p>We identify a common set of challenges in managing provenance information across the <it>pre-publication </it>and <it>post-publication </it>phases of data in the translational research lifecycle. We define the semantic provenance framework (SPF), underpinned by the Provenir upper-level provenance ontology, to address these challenges in the four stages of provenance metadata:</p> <p>(a) Provenance <b>collection </b>- during data generation</p> <p>(b) Provenance <b>representation </b>- to support interoperability, reasoning, and incorporate domain semantics</p> <p>(c) Provenance <b>storage </b>and <b>propagation </b>- to allow efficient storage and seamless propagation of provenance as the data is transferred across applications</p> <p>(d) Provenance <b>query </b>- to support queries with increasing complexity over large data size and also support knowledge discovery applications</p> <p>We apply the SPF to two exemplar translational research projects, namely the Semantic Problem Solving Environment for <it>Trypanosoma cruzi </it>(<it>T.cruzi </it>SPSE) and the Biomedical Knowledge Repository (BKR) project, to demonstrate its effectiveness.</p> <p>Conclusions</p> <p>The SPF provides a unified framework to effectively manage provenance of translational research data during pre and post-publication phases. This framework is underpinned by an upper-level provenance ontology called Provenir that is extended to create domain-specific provenance ontologies to facilitate provenance interoperability, seamless propagation of provenance, automated querying, and analysis.</p

A Semantic Problem Solving Environment for Integrative Parasite Research: Identification of Intervention Targets for Trypanosoma cruzi

Effective research in parasite biology requires analyzing experimental lab data in the context of constantly expanding public data resources. Integrating lab data with public resources is particularly difficult for biologists who may not possess significant computational skills to acquire and process heterogeneous data stored at different locations. Therefore, we develop a semantic problem solving environment (SPSE) that allows parasitologists to query their lab data integrated with public resources using ontologies. An ontology specifies a common vocabulary and formal relationships among the terms that describe an organism, and experimental data and processes in this case. SPSE supports capturing and querying provenance information, which is metadata on the experimental processes and data recorded for reproducibility, and includes a visual query-processing tool to formulate complex queries without learning the query language syntax. We demonstrate the significance of SPSE in identifying gene knockout targets for T. cruzi. The overall goal of SPSE is to help researchers discover new or existing knowledge that is implicitly present in the data but not always easily detected. Results demonstrate improved usefulness of SPSE over existing lab systems and approaches, and support for complex query design that is otherwise difficult to achieve without the knowledge of query language syntax

Bilingual acoustic voice variation: the case of Sorani Kurdish-Persian speakers

Many individuals around the world speak two or more than two languages. This phenomenon adds a fascinating dimension of variability to speech, both in perception and production. But do bilinguals change their voice when they switch from one language to the other? It is typically assumed that while some aspects of the speech signal vary for linguistic reasons, some indexical features remain unchanged across languages. Yet little is known about the influence of language on within- and between-speaker vocal variability. The present study investigated how acoustic parameters of voice quality are structured in two languages of a bilingual speaker and to what extent such features may vary between bilingual speakers. For this purpose, speech samples of 10 simultaneous Sorani Kurdish-Persian bilingual speakers were acoustically analyzed. Following a psychoacoustic model proposed by Kreiman (2014) and using a series of principal component analyses, we found that Sorani Kurdish-Persian bilingual speakers followed a similar acoustic pattern in their two different languages, suggesting that each speaker has a unique voice but uses the same voice parameters when switching from one language to the other

The Effectiveness of Perceptual Skills Reconstruction Program on Working Memory, perceptual reasoning, and Math Performance of Students with Specific Math Learning disorder

Aim: this survey is done with the objective of studying the effectiveness of Perceptual Skills reconstruction program on perceptual reasoning, working memory, and math performance of students with specific math learning disorder.Methods:this empirical study includes pretest-posttest and control group. The statistical population included third grade students with specific math learning disorder in Joghatay. They were selected by the third grade teachers by performing a diagnostic test of math disorder and randomly assigned to 30 subjects and they were randomly assigned to the treatment and control groups. The treatment group received 16 sessions of training program. Data collection was done by the use of the Key Math Test, the Raven Test, Wechsler–IV Perceptual Reasoning Scale, Math Disorders Diagnostic test, and Stanford-Bine's Working Memory Scale. Results: Data analysis using mixed variance analysis with repeated measurements showed that intervention program implementation was significantly effective on nonverbal working memory, perceptual reasoning and its mathematical performance in experimental students at post-test stage and The observed effects in the follow-up phase remained stable. But there was no difference between the treatment and control groups in the verbal working memory variable.Conclusion: based on this, it can be concluded that the training of this program is effective on math performance, nonverbal working memory and perceptual reasoning of students with specific learning math disorder and can be considered as an effective approach in the treatment ofspecific learning math disorder