Currently, there is no industry support for T1 (basic to clinical) behavioral research and therefore little incentive for basic and clinical behavioral scientists to work together to develop and test new, innovative strategies for changing HLBS-related behaviors based on basic behavioral science findings on motivation, perception, cognition and social relationships. Bringing together collaborative, interdisciplinary teams of basic behavioral scientists and clinically-oriented behavioral researchers could spur development and testing of innovative new approaches to difficult HLBS-related behavioral problems such as obesity, unhealthy diets, sedentary lifestyles, smoking and non-adherence to preventive and therapeutic HLBS regimens.

New research in the behavioral sciences is elucidating the basic psychological, cognitive, social and behavioral processes underlying behavior and behavior change. Findings in this area could be developed into new strategies targeting problematic HLBS-related behaviors, but a mechanism for developing and testing novel ideas is needed. Networks designed to bring together basic and clinically-oriented behavioral researchers can enable better understanding of the bases of HLBS-related behaviors and accelerate the translation of findings into new approaches.
Adopting and maintaining healthy habits and lifestyles – such as eating healthy diets, engaging in regular physical activity, stopping smoking, and regularly taking prescribed medications – are crucial to heart, lung, blood and sleep (HLBS) health (Akesson et al, 2014; Mozaffarian, 2014). However, for most people, engaging in and maintaining a healthy lifestyle is challenging. Interventions designed to promote behavior change have had limited success, often influencing individuals over the short-term but failing to alter behaviors over longer periods of time, which is necessary to realizing the full benefits of a healthy lifestyle. Underlying the problematic behaviors associated with HLBS-related behavioral risk factors are fundamental psychological, motivational, cognitive and social processes that represent promising targets for the development of new, more effective behavioral interventions. For example, basic behavioral scientists are investigating the role of poor executive function in unhealthy eating behavior and exploring new ways to address the "self-control" failures that lead to impulsive eating.

However, unlike the biomedical arena where the translational pathway from basic science to clinical application is supported by both NIH and industry, there is no industry support and relatively little NIH funding devoted to T1 behavioral research -- i.e., research translating basic behavioral science findings into clinically significant behavioral interventions. As a result, basic behavioral science researchers have little incentive to collaborate with clinical researchers to develop and test novel behavioral treatments. Bringing together collaborative, interdisciplinary teams of basic behavioral scientists and clinically-oriented behavioral researchers could spur development and testing of innovative new approaches to difficult HLBS-related behavioral problems.

A compelling question is how to bring together these disparate researchers over a long enough time frame to enable them to identify, develop and testing new strategies for tackling resistant behavioral problems. One way to address this question is to fund a network of "behavior change labs," each of which brings together teams of basic behavioral scientists who are investigating the bases of behavior and behavior change with clinical researchers interested in designing, optimizing and testing novel ideas for tackling the difficult behavioral problems represented by obesity, unhealthy diets, sedentary lifestyles, smoking and non-adherence to medications used to prevent or treat HLBS diseases and disorders.

Inherited monogenic hematologic diseases such as hemophilia, beta-thalassemia and sickle cell disease are prime targets for future application of genome editing technology. However, studies are still needed to advance our understanding of the biology of genome editing as well as determine which other disorders are amenable to genome editing correction. Emphasis on preclinical research that focuses on determining the accuracy, safety and efficiency of this technology in order to help minimize off-target mutations and reduce toxicity, is essential for effective translation of this technology into the clinic. Once preclinical efficacy is established, support will be needed for clinical vector production, toxicity testing of the vectors/reagents used, and the performance of clinical trials. The gene correction strategies developed for inherited disorders will also be attractive for other hematologic diseases, and autoimmune disorders like lupus, rheumatoid arthritis, and type I diabetes). There is also a critical need for supporting preclinical validation studies, scale-up and GMP cell manufacturing, all of which could be shared infrastructures across multiple diseases in the NHLBI portfolio.

• Provide new knowledge for implementing strategies that will tackle inequities
• Provides opportunity to create multidiscipline research communities focused on health inequities
• Will gain momentum from federal and national implementation institutions to promote effective interventions to address health inequities

• Capitalizing on new methods, metrics and tapping big data could provide a promising platform to use systems science to better understand the barriers to eliminating health inequities in a short timeframe
• Identifying barriers will allow investigators to devise innovative implementation strategies that can reduce health inequities
• NHLBI could encourage communities of researchers to use team science to both identify barriers and link with other teams to implement strategies to reduce and eliminate barriers
• Established NHLBI health inequities Think Tank and space for developing innovative strategies to address health inequities.

Translation alone is fishing in the dark, with low likelihood of success. Mechanistic alone is just science for the sake of science, because academic careers are promoted by high profile publications. The NHLBI can change this!

The NHLBI can change this by giving dual awards to qualified teams. Basically and R01 for the mechanistic work ($ 250 K per year for 5 years) coupled with funding for translational research (probably $ 6 m for a typical program to reach phase I)

Having an increased number of researchers able to connect dots across the continuum of translational research should increase overall success of translation of ideas into health.

This requires "rearranging" of already existing elements. Within 5-10 years of running specifically designed re/cross -training programs, the effects might be widely visible.
Basic scientists usually do not keep up with the latest outcomes of important clinical studies, and thus might miss important starting points for new basic research (e.g., negative trials that suggest the need for new hypotheses). The great majority of clinical scientists do not attend basic scientific sessions because are turned off by the specialized (dense/obscure) scientific terms used. Those who are interested in being translators have a hard time integrating and surviving in the "opposite camp" (i.e., at many medical schools, basic scientists are expected to bring in all their salary in a clinical department, and clinicians get little protected time for basic research)

There are two areas of impact. First is to develop a better understanding between the effect of genetic variation on RNA processing and how individual differences in RNA processing translate into "phenotype" such as disease causation, disease modification, disease susceptibility, and positive or negative responses to therapies. Genes are filled with cis acting elements that are required for specific patterns of RNA processing. Variation that affects these cis elements are now known to produce different splice variants or mRNAs with different stabilities between individuals. This mechanism translating genotype to phenotype has not been explored. Second is to understand the RNA processing networks as well as we understand transcriptional networks during heart development and in heart disease. This understanding is very likely to provide previously unknown therapeutic approaches and targets.

The high through put approaches available to compare genome and transcriptome sequences,computational approaches to predict the cis elements for RNA processing, high throughput analysis for RNA binding proteins and RNA structure have provided the tools necessary to perform genome-transcriptome comparisons as a starting point. Current exome/genome analysis ignores the influence of genetic variation in RNA processing. The tools are available. The first challenge is to decide on the question and there are two areas: One- what is the role of RNA processing in heart disease/how can RNA processing be used as a therapeutic target and Two - how much does differences in RNA processing (in addition to transcription) contribute to individual phenotypic differences relevant to disease?

NHLBI is currently investing heavily in training epidemiologists at the PhD and postdoctoral level, but may be losing those researchers during the transition to independence and mid-career stages. Compared to those with clinical backgrounds or basic science skills, PhD epidemiologists rarely have alternative resources to fall back on when NIH funding is reduced and may have less interest in joining industry if their primary research interest is improving public health at the population level. This may make epidemiologists particularly vulnerable to leaving the field of health research. Dedicated funding that prioritizes human studies and population level research would help retain well-trained epidemiologists whose primary dedication is to improving chronic disease outcomes.

While the current level of funding is a challenge to everyone working in science right now, adding funding mechanisms specifically for epidemiology like those that already exist for clinician scientists and pre-clinical research could play an essential role in maintaining the pace of innovation and implementation of research.

If we could develop less costly and time consuming cell and animal models of COPD that reflect meaningful subphenotypes, we would be able to not only probe basic mechanism but also have reliable test platforms for candidate therapies.
There is typically a major obstacle between the acquisition of big data from observational disease cohorts, often broad but superficial, and the translation of these findings to basic discovery efforts. The clinical researchers speak a different language than the basic investigators and traversing this chasm with grant enticements might prove helpful.

This would require some suspension of the classic mechanistic, hypothesis driven proposals to develop these research tools.
There would need to be some reconstruction of study sections to permit these combined clinical-basic grants. The translational PPG was in keeping with this but should be reinforced with smaller grant programs such as RO1 level grants.

This innovative and transformative proposal could improve tolerance to many different types of transplants.

In 2002, Hochedlinger and Jaenisch (Nature 415:1035-1038) created a mouse by nuclear transplantation from a mature B-cell. This was proof of principle that the immune system can be reprogrammed entirely. Since then there has been little work in this area, but Reprogramming Immune System Cells (RISC) is risky but promising.
A second approach involves mechanisms that cancer cells use to evade immune detection. While most cancer research works to restore immune competence for therapy, the basic biology of evading immune detection could be exploited to improve tolerance. These approaches could be tested in an animal model in 5 years.

Answering this question would lead to timely implementation and dissemination of new knowledge that will be available to address the current heart, lung, blood, sleep health burden.
One of NHLBI’s current strategic plan’s goals is to translate research knowledge for use in populations so that it has significant positive health impacts and provides a return on investment from discovery and early translation research. Currently, only a fraction of new knowledge yielded from proven effective early stage translation research is being used in the real world – resulting in an avoidable disease burden. Late stage translation (T4) research studies the implementation of proven-effective interventions at a multi-level to include populations, communities, healthcare systems, providers, families and patients. Conducting T4 research requires development of comprehensive research teams across communities, public health and health care delivery systems, families, and patients along with unique translation T4 research methods and metrics. Because resource intensive infrastructure is needed to conduct high quality T4 research and this infrastructure is not widely or readily available, a platforms and network for conducting protocols will prove efficient and provide high quality standard outputs.

The NHLBI Health Inequities Think Tank highly recommended that creating T4 translation research platforms and networks would meet the needs of the scientific community enabling them to respond to the heart, lung, blood, sleep health burden.

• Assure that early stage translation research will be suitable for implementation in real world setting
• Aligns the research interventions from T1-T3 research to those appropriate to T4 research
• Potential to focus early stage research in key high burden areas
• Provides research community an understanding of the connections from early stage to late stage translation research which will potentially refine research strategies and directions at all levels

• Promote the importance of translation to population of heart, lung, blood, and sleep researcher to broader research community
• Potential for more T4 research contributions for guiding investment into translation research from T1-T3
• Provide avenues for T1-T3 investigators to translate their ideas into positive outcomes for population health
• Successful T4 research will stimulate feedback loop and identify opportunities for early translation research

• Ability to determine how much of an evidence based intervention can be sustained in real world settings.
• Add utilization of D&I frameworks to researcher’s core competency training skills.
• Promote long term sustainability of evidence based interventions.

• Researchers from the 2014 NIH’s Annual Conference on the Science of Dissemination and Implementation: Transforming Health Systems to Optimize Individual and Population Health presented compelling evidence that dissemination and implementation frameworks are an effective means to scaling up evidence based interventions.

• Decrease the burden of heart, lung, blood, and sleep diseases in studies funded by PEPFAR in HIV populations
• Lessons learned could be expanded to HIV populations outside of Africa
• T4 translation interventions in these populations could help reduce risk factors for heart, lung, blood, and sleep diseases leading to better health outcomes

• PEPFAR has identified and recruited existing HIV populations in Africa which can be leveraged by NHLBI for heart, lung, blood, and sleep chronic disease research
• Infrastructure that has received PEPFAR investments can also be leveraged to undertake T4 translation interventions

High quality T4 research methods and metrics are needed to move the field of T4 translation research forward while linking large data sets from different sources.

Demand for high quality methods, metric and evaluation of T4 translation research interest is growing and needs to be addressed immediately to move the field forward.
Recent IOM/NRC studies recommended that the NIH and other research funding agencies support the development of more refined analytic methods and study designs for cross-national health research. These methods should include innovative study designs, creative uses of existing data, and novel analytical approaches to better elucidate the complex causal pathways. The T4 field has some specific metrics including acceptability, reach, adoption, appropriateness, feasibility, fidelity, cost, penetration, and sustainability, each with its standard measurement approach. In addition to a rigorous study design, including these metrics along with population level impact direct measures (e.g., morbidity, mortality) and intermediate measures (e.g. blood pressure reduction) will be critical to assess what has been accomplished and to define success. Finally, measuring the overall impact of new knowledge generated from T4 research is challenging because publication bibliometrics of high impact scholarly journals may not fully capture it.