scholarly journals Toward a science of translational science

2017 ◽  
Vol 1 (4) ◽  
pp. 253-255 ◽  
Author(s):  
Caleb Smith ◽  
Roohi Baveja ◽  
Teri Grieb ◽  
George A. Mashour
Keyword(s):  
Translational Research ◽  
Large Scale ◽  
Network Science ◽  
Systematic Investigation ◽  
Industrial Engineering ◽  
Translational Science ◽  
Exponential Increase ◽  
Large Scale Networks ◽  
The Impact

Translational research as a discipline has experienced explosive growth over the last decade as evidenced by significant federal investment and the exponential increase in related publications. However, narrow project-focused or process-based measurement approaches have resulted in insufficient techniques to measure the translational progress of institutions or large-scale networks. A shift from traditional industrial engineering approaches to systematic investigation using the techniques of scientometrics and network science will be required to assess the impact of investments in translational research.

A Scalable Redefined Stochastic Blockmodel

2021 ◽  
Vol 15 (3) ◽  
pp. 1-28
Author(s):  
Xueyan Liu ◽  
Bo Yang ◽  
Hechang Chen ◽  
Katarzyna Musial ◽  
Hongxu Chen ◽  
...  
Keyword(s):  
Large Scale ◽  
Network Science ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Real World Data ◽  
Computational Overhead ◽  
Stochastic Blockmodel ◽  
Np Hard Problem ◽  
Large Scale Networks ◽  
The Cost

Stochastic blockmodel (SBM) is a widely used statistical network representation model, with good interpretability, expressiveness, generalization, and flexibility, which has become prevalent and important in the field of network science over the last years. However, learning an optimal SBM for a given network is an NP-hard problem. This results in significant limitations when it comes to applications of SBMs in large-scale networks, because of the significant computational overhead of existing SBM models, as well as their learning methods. Reducing the cost of SBM learning and making it scalable for handling large-scale networks, while maintaining the good theoretical properties of SBM, remains an unresolved problem. In this work, we address this challenging task from a novel perspective of model redefinition. We propose a novel redefined SBM with Poisson distribution and its block-wise learning algorithm that can efficiently analyse large-scale networks. Extensive validation conducted on both artificial and real-world data shows that our proposed method significantly outperforms the state-of-the-art methods in terms of a reasonable trade-off between accuracy and scalability. 1

scholarly journals Towards Best Practice in Hair Metabolomic Studies: Systematic Investigation on the Impact of Hair Length and Color

Metabolites ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 381
Author(s):  
Lisa Eisenbeiss ◽  
Tina M. Binz ◽  
Markus R. Baumgartner ◽  
Thomas Kraemer ◽  
Andrea E. Steuer
Keyword(s):  
Large Scale ◽  
Best Practice ◽  
Data Interpretation ◽  
Systematic Investigation ◽  
Hair Color ◽  
Large Scale Analysis ◽  
Retrospective Monitoring ◽  
Hair Samples ◽  
Endogenous Metabolites ◽  
The Impact

Untargeted metabolomic studies are used for large-scale analysis of endogenous compounds. Due to exceptional long detection windows of incorporated substances in hair, analysis of hair samples for retrospective monitoring of metabolome changes has recently been introduced. However, information on the general behavior of metabolites in hair samples is scarce, hampering correct data interpretation so far. The presented study aimed to investigate endogenous metabolites depending on hair color and along the hair strand and to propose recommendations for best practice in hair metabolomic studies. A metabolite selection was analyzed using untargeted data acquisition in genuine hair samples from different hair colors and after segmentation in 3 cm segments. Significant differences in metabolites among hair colors and segments were found. In conclusion, consideration of hair color and hair segments is necessary for hair metabolomic studies and, subsequently, recommendations for best practice in hair metabolomic studies were proposed.

scholarly journals 2305

2017 ◽  
Vol 1 (S1) ◽  
pp. 47-47
Author(s):  
Gayathri Devi ◽  
Ranjan Sudan ◽  
Stephanie Freel ◽  
Laura Fish
Keyword(s):  
Focus Groups ◽  
Translational Research ◽  
Interdisciplinary Approach ◽  
Health Care Research ◽  
Assessment Tools ◽  
Grant Writing ◽  
Team Science ◽  
Translational Science ◽  
Duke University ◽  
The Impact

OBJECTIVES/SPECIFIC AIMS: To improve translational research, we have developed a program called Duke Multidisciplinary Education and Research in Translational Sciences (Duke MERITS). Duke MERITS will facilitate cross-disciplinary collaboration among faculty involved in foundational, clinical and/or health care research and in turn also prepare them to train the next generation of translational researchers. METHODS/STUDY POPULATION: The program aims are (1) to define metrics and outcomes measures so faculty can track their progress and identify impact of their collaborative research in translational sciences; (2) to offer a multi-modal faculty development series to promote team science, improve didactic teaching, and incorporate innovative resources to promote interdisciplinary approach to translational research; (3) to provide module-based hands-on-training sessions in bench to bedside research and training in translational grant writing to facilitate the development of multidisciplinary research collaborations. The present study describes results from Aim 1 and includes (a) development of baseline outcome assessment tools necessary to gauge the impact of our programs on both the participating faculty and the research culture within Duke University, (b) impact of a specific course offering in Translational Medicine. In order to achieve this, we conducted multiple focus group sessions with faculty self-identified as junior-, mid-, or advanced-career, a mixed group at any career level and included a group of graduate students and postdoctoral trainees to study the impact of a graduate level course in Translational Aspects of Pathobiology. The activities during these translational science focus groups were designed to define what successful translational science is, to determine what resources support translational Science at Duke, and to decide what resources we need in order to enhance Duke’s position as a leader in research and scientific education. RESULTS/ANTICIPATED RESULTS: We identified that translational science is changing standards while incorporating leadership, teamwork, collaborations, and movement primarily focusing on the overall goal of improving all aspects of health. Participants categorized their field of study and the fields of their coparticipants most frequently as basic discovery and a combination of intervention and health services. The most frequently identified pros/benefits of performing translational science at Duke include industry connections, collaborations with other departments resulting in disciplines being bridged, improving patient care, and access to resources as well as money. The most frequently identified cons/barriers of performing translational science includes the expensiveness, silos, and lack of resources willing to absorb risks. DISCUSSION/SIGNIFICANCE OF IMPACT: The identification of these defined factors from the focus groups has allowed us to issue a comprehensive, sliding Likert scale-based anonymous survey from the secure RedCap system and is being rolled out throughout Duke University, including schools of medicine, nursing, Trinity, biomedical engineering. We envision that Duke MERITS education program will facilitate interprofessional efforts, which we define as a team science approach to identify the clinical “roadblock” and then seek an innovative approach or technology to help overcome this “roadblock”? It can facilitate institutional and departmental recognition in faculty career development. The common goal is to gain fundamental new insights that will result in significant improvement of the existing “standard of care” and meet the challenges of dwindling extramural support.

scholarly journals Disentangling shock diffusion on complex networks: identification through graph planarity

2020 ◽  
Vol 8 (3) ◽  
Author(s):  
Sudarshan Kumar ◽  
Tiziana Di Matteo ◽  
Anindya S Chakrabarti
Keyword(s):  
Large Scale ◽  
Developed Countries ◽  
Shock Propagation ◽  
Propagation Mechanism ◽  
Dynamical Process ◽  
Economic Networks ◽  
Closed Loops ◽  
The Usa ◽  
Large Scale Networks ◽  
The Impact

Abstract Large scale networks delineating collective dynamics often exhibit cascading failures across nodes leading to a system-wide collapse. Prominent examples of such phenomena would include collapse on financial and economic networks. Intertwined nature of the dynamics of nodes in such network makes it difficult to disentangle the source and destination of a shock that percolates through the network, a property known as reflexivity. In this article, we propose a novel methodology by combining vector autoregression with an unique identification restrictions obtained from the topological structure of the network to uniquely characterize cascades. In particular, we show that planarity of the network allows us to statistically estimate a dynamical process consistent with the observed network and thereby uniquely identify a path for shock propagation from any chosen epicentre to all other nodes in the network. We analyse the distress propagation mechanism in closed loops giving rise to a detailed picture of the effect of feedback loops in transmitting shocks. We show usefulness and applications of the algorithm in two networks with dynamics at different time-scales: worldwide GDP growth network and stock network. In both cases, we observe that the model predicts the impact of the shocks emanating from the USA would be concentrated within the cluster of developed countries and the developing countries show very muted response, which is consistent with empirical observations over the past decade.

Decentralized Search and the Clustering Paradox in Large Scale Information Networks

2012 ◽  
pp. 29-46
Author(s):  
Weimao Ke
Keyword(s):  
Information Retrieval ◽  
Large Scale ◽  
Information Networks ◽  
Search Performance ◽  
Retrieval Models ◽  
Information Spaces ◽  
Decentralized Search ◽  
Key Issues ◽  
Large Scale Networks ◽  
The Impact

Amid the rapid growth of information today is the increasing challenge for people to navigate its magnitude. Dynamics and heterogeneity of large information spaces such as the Web raise important questions about information retrieval in these environments. Collection of all information in advance and centralization of IR operations are extremely difficult, if not impossible, because systems are dynamic and information is distributed. The chapter discusses some of the key issues facing classic information retrieval models and presents a decentralized, organic view of information systems pertaining to search in large scale networks. It focuses on the impact of network structure on search performance and discusses a phenomenon we refer to as the Clustering Paradox, in which the topology of interconnected systems imposes a scalability limit.

scholarly journals Assessing Participant Group Affiliation and Attitudes Towards CTSI Services

2019 ◽  
Author(s):  
Linda Behar-Horenstein ◽  
Huibin Zhang
Keyword(s):  
Mixed Methods ◽  
Translational Research ◽  
Sentiment Analysis ◽  
Frequency Function ◽  
Translational Science ◽  
Group Affiliation ◽  
Survey Question ◽  
Participant Group ◽  
Participant Perspectives ◽  
The Impact

Analyzing open-ended survey text responses holds the capacity to yield greater insight about participants’ perceptions of clinical translational science institute (CTSI) initiatives. Few translational research studies have explored their effectiveness. The aim of this mixed methods analysis was to assess participant perspectives of the impact and effectiveness of our CTSI program and services. We selected two open-ended survey question items (how CTSI benefitted research, and the most important impact of the research facilitated by the CTSI) from a larger set and compared responses by participant affiliations (clinical/non-clinical; lab/non-lab). We used a three-step analysis. First, nodes were generated using NVivo word frequency function. Next, with the aid of Python, we used sentiment analysis to classify each node (as positive, negative, or neutral) to indicate participant ratings toward their experiences with the CTSI and computed the average differences between groups. Third, we selected nodes that met pre-established criteria and report the qualitative distinctions. We recommend using precisely worded open-ended questions in future annual surveys or administering a survey using only opened-ended questions every six months.

scholarly journals Cerebral cartography and connectomics

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140173 ◽  
Author(s):  
Olaf Sporns
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Network Science ◽  
Temporal Dynamics ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Hierarchical Organization ◽  
Large Scale Networks ◽  
Definition Of ◽  
The Brain

Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics.

scholarly journals Improving clinical and translational research training: a qualitative evaluation of the Atlanta Clinical and Translational Science Institute KL2-mentored research scholars program

2016 ◽  
Vol 65 (1) ◽  
pp. 23-31 ◽  
Author(s):  
Dawn L Comeau ◽  
Cam Escoffery ◽  
Ariela Freedman ◽  
Thomas R Ziegler ◽  
Henry M Blumberg
Keyword(s):  
Career Development ◽  
Translational Research ◽  
Research Training ◽  
Minority Group ◽  
Junior Faculty ◽  
Translational Science ◽  
Underrepresented Minority ◽  
Science Institute ◽  
The Impact ◽  
Clinical And Translational Research

A major impediment to improving the health of communities is the lack of qualified clinical and translational research (CTR) investigators. To address this workforce shortage, the National Institutes of Health (NIH) developed mechanisms to enhance the career development of CTR physician, PhD, and other doctoral junior faculty scientists including the CTR-focused K12 program and, subsequently, the KL2-mentored CTR career development program supported through the Clinical and Translational Science Awards (CTSAs). Our evaluation explores the impact of the K12/KL2 program embedded within the Atlanta Clinical and Translational Science Institute (ACTSI), a consortium linking Emory University, Morehouse School of Medicine and the Georgia Institute of Technology. We conducted qualitative interviews with program participants to evaluate the impact of the program on career development and collected data on traditional metrics (number of grants, publications). 46 combined K12/KL2 scholars were supported between 2002 and 2016. 30 (65%) of the 46 K12/KL2 scholars are women; 24 (52%) of the trainees are minorities, including 10 (22%) scholars who are members of an underrepresented minority group. Scholars reported increased research skills, strong mentorship experiences, and positive impact on their career trajectory. Among the 43 scholars who have completed the program, 39 (91%) remain engaged in CTR and received over $89 000 000 as principal investigators on federally funded awards. The K12/KL2 funding provided the training and protected time for successful career development of CTR scientists. These data highlight the need for continued support for CTR training programs for junior faculty.

Exercise and Cognition—2016

2017 ◽  
Vol 29 (1) ◽  
pp. 31-34 ◽  
Author(s):  
Phillip D. Tomporowski
Keyword(s):  
Physical Activity ◽  
Translational Research ◽  
Large Scale ◽  
Physical Activity Intervention ◽  
Policy Makers ◽  
School Settings ◽  
Academic Settings ◽  
Research Findings ◽  
Definition Of ◽  
The Impact

Physical activity is purported to promote children’s brain health and enhance mental development (1). Three studies were selected for review because of their focus on issues that challenge translational research applications in exercise pediatric science. While some disagreement exists concerning the definition of translational research, most suggest that translational interventions focus on the uptake, implementation, and sustainability of research findings within standard care (2). Translational researchers typically highlight differences that exist between efficacy experiments, which provide evidence that a specific intervention works, and effectiveness experiments, which show that the intervention will reap benefits under real-world conditions. Results obtained from laboratory-based efficacy studies that have examined the relation between exercise and cognition led researchers (3,4) and policy makers to consider the importance of physical activity in school settings. Large-scale studies that assess the impact of various types of school based physical activity intervention on children’s cognitive and academic performance have begun. The initial results have been uneven and suggestive of a lack of benefit for children in authentic school settings. Before drawing such conclusions, however, it will be important for researchers and practitioners to recognize the methodological and measurement issues that challenge attempts to employ laboratory methodologies to academic settings.

scholarly journals Transforming Competitiveness by Introducing Digital Platforms

2021 ◽  
Author(s):  
Olga NOSOVA ◽  
Volodymyr LYPOV
Keyword(s):  
Large Scale ◽  
Network Effects ◽  
Competitive Advantages ◽  
Digital Platforms ◽  
Doing Business ◽  
Active Dissemination ◽  
Specific Factors ◽  
Large Scale Networks ◽  
The Cost ◽  
The Impact

The purpose of the proposed paper is to study the specific factors shaping the benefits of information platforms as an innovative institutional form and model of doing business. Active dissemination of the business model of online platforms radically transforms the competitive landscape of the market environment. The task of determining the sources and mechanisms for studying the changes that are taking place is being updated. New areas of competition include competition between hierarchical and network structures, between global «structuring» platforms, competition in dominant platform ecosystems, the interaction between platforms operating in competitive markets, competition between organizers and users, and between platform users. The impact of platforming on cross-industry, regional and international competition is determined. The sources of competitive advantages of platforms are investigated. These include reliance on data as the main factor of production, changing the cost structure of entering the market; the possibility of building large-scale networks, niche specialization, combining the effects of increasing the scale of production and demand, multihoming, multi productivity, network effects.

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