Science, Art, Litt, Science based Art & Science Communication
The benefits of convergent science, technology and research
When we think of research and its meaning and analysis, we first think about Objectives of Research. Like...
*verifing and testing important facts
*analysing an event or process or phenomenon to identify the cause and effect relationship
to develop new scientific tools, concepts and theories to solve and understand scientific and nonscientific problems
*finding solutions to scientific, nonscientific and social problems and
*overcoming or solving the problems occurring in our every day life.
To achieve these goals, we have to think about various creative and smart methods that can take us to our destination. One such new approach is 'convergent research'.
Convergence is a coming together of two or more distinct entities or phenomena. Convergence is increasingly prevalent in the IT world; in this context the term refers to the combination of two or more different technologies in a single device. Taking pictures with a cell phone and surfing the Web on a television are two of the most common examples of this trend.
Convergence may influence laymen to accept new technologies. According to some studies, people who aren't computer literate are more likely to embrace the Internet, video-on-demand, and so on if they can -- at least initially -- access these technologies through their televisions. In general, TV is familiar and non-threatening. Displays are large and TVs are easy to operate, requiring almost no training. Personal computers, in spite of their graphical user interfaces tend to be more text-oriented, highly interactive, oriented in terms of purpose and content toward business and education uses. Displays are smaller. Computers can be very challenging to use and usually require formal education or a certain personal learning curve. Computer-television convergence is a solution to such problems.
When two or more lines of research begin to converge on a similar result this is referred to as convergent evidence. Convergent research is the designed use of more than one theoretical approach to examine a problem space. Convergent research in science is more integration of physical, life sciences, chemical and other fields for revolutionary advances in biomedical research. It entails collaboration among research groups but, more deeply, the integration of disciplinary approaches that were originally viewed as separate and distinct. This merging of technologies, processes and devices into a unified whole will create new pathways and opportunities for scientific and technological advancement.
Advances in information technology, materials, imaging, nanotechnology and related fields — coupled with advances in computing, modeling and simulation — have transformed the physical sciences, so are they are beginning to transform life science. The result is critical new biology-related fields, such as bioengineering, computational biology, synthetic biology and tissue engineering.
At the same time, biological models (understanding complex, self-arranged systems) are already transforming engineering and the physical sciences, making possible advances in biofuels, food supply, viral self assembly and much more. Biomimicry is now revolutionizing modern technology.
Can lost limbs be regrown? Can cancers be detected early with blood or urine tests, instead of invasive biopsies?Can drugs be delivered via nanoparticles to specific tissues or even cells, minimizing unwanted side effects? While such breakthroughs may sound futuristic, scientists are already exploring these and other promising techniques.
The key to bringing these dreams of scientists to fruition will be strategic and sustained support for “convergence”: the merging of approaches and insights from historically distinct disciplines such as engineering, physics, computer science, chemistry, mathematics, and the life sciences. How wonderful that would be?
Convergence science has by now advanced across many fronts, from nanotechnology to regenerative tissue and found to be very potent. More than just interdisciplinary research, convergence entails the active integration of these diverse modes of inquiry into a unified pursuit of advances that will transform health and other sectors, from agriculture to energy.
The fields that are being covered by convergence research are three major disease areas — brain disorders, infectious diseases and immunology, and cancer — and promising convergence-based approaches to tackling them and four emerging technology categories: advanced imaging in the body, nanotechnology for drug and therapy delivery, regenerative engineering, and big data and health information technology.
A sense of their transformative potential: Convergence techniques could enable rewiring the genes of mosquitoes to eliminate Zika, dengue, and malaria. They could help solve the emerging threat of drug-resistant bacterial strains, which infect millions of people in the world every year. Convergence-based immunotherapy could activate a person’s immune system to fight cancer, reprogramming a person’s T-cells or antibodies to find and attack tumor cells. Big-data techniques could be used to generate and analyze huge amounts of data on people’s exposures to industrial chemicals, environmental toxins, and infectious agents, creating a new field of “chemistry of nurture,” to complement the “chemistry of nature” developed by the documentation of the human genome.
Realizing the full potential of the convergence revolution will require ambitious and strategic coordination and collaboration across industry, government, and academia.
To develop “cultures of convergence” within academic institutions, cross-department hiring and tenure review, convergence “cluster hiring” and career grants, and new PhD programs wherein students design their own degree programs across disciplinary boundaries are the steps that are being taken in some countries. Students are being educated, collaborations encouraged, and resources committed to make sure convergence thrives.
We are into thrilling times of inventions, discoveries and solving problems. Watch this space for more exciting information.
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When biology inspires mathematics: Hidden symmetries explain why widely used evolutionary methods can give false answers
Why do some groups of organisms contain thousands of species while others have only a handful? Evolutionary biologists have spent decades trying to answer this question using mathematical models that estimate how biological traits and environmental factors influence the formation and extinction of species.
These models have become a cornerstone of modern biology and have been used in more than 1,000 scientific studies. Yet the models carry a known weakness: They can sometimes lead scientists to the wrong conclusions. For years, no one fully found out why.
Several years ago, researchers found that many evolutionary models can generate exactly the same observations even when they rest on entirely different assumptions about evolutionary history. This meant that scientists could unknowingly reach different conclusions that were all equally consistent with the same data. Whether the same ambiguity also affected the more sophisticated models used to study how traits shape biodiversity remained unclear because their mathematics was too complex to analyze directly.
Sergei Tarasov at the Finnish Museum of Natural History and Josef Uyeda at Virginia Tech approached the problem from a different angle. Their path to the solution started with a simple but unusual question: Imagine three apples—one red, one light green and one dark green. Should the two green apples be grouped together or treated as different colors? The researchers ran into the same classification puzzle while studying beetle anatomy.Searching for an answer led them to lumpability, a mathematical concept introduced in the 1960s that defines when different states of a Markov model can be safely grouped together without changing how a system behaves. Building on it, they unexpectedly discovered a new way of representing Markov models, one of the most fundamental classes of stochastic models used across science.
They showed that every discrete-state Markov model can be rewritten as an equivalent hidden-state model, a decomposition they call Hidden Expansion. Although the rewritten model looks larger, it is built from simple, identical mathematical components. This representation exposed previously hidden mathematical symmetries and turned an intractable problem into a solvable one.
This mathematical property had gone unnoticed despite decades of research on Markov models.The new decomposition allowed the researchers to answer the question that had resisted mathematical analysis for years. They showed that the same hidden symmetries also affect the sophisticated models used to study biodiversity and that the misleading conclusions these models sometimes produce are not isolated statistical mistakes. Instead, they stem from a deeper mathematical ambiguity built into the models themselves.
Scientific discoveries often begin with advances in mathematics that later transform biology. This study followed the opposite path. A biological question about how to represent anatomical traits of beetles led to a new mathematical discovery, which in turn solved a long-standing problem in evolutionary biology.It's a wonderful example of biology and mathematics driving each other forward.
Sergei Tarasov et al, Unidentifiability and false-positive inference in state-dependent diversification models, Nature Communications (2026). DOI: 10.1038/s41467-026-75829-5
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