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Image Name: Vitis vinifera :Vitis vinifera , the common grape vine, is a woody, deciduous, climbing plant

native to the Mediterranean, Central Europe, and southwestern Asia, and is widely cultivated on every continent

except Antarctica. With a diversity of between 5,000 and 10,000 varieties as of 2012, only a small number are

commercially signiﬁcant for wine and table grape production. Its grapes can be eaten fresh, dried into raisins,

sultanas, or currants, or processed into juice, wine, and vinegar. Grape leaves are also used in many cuisines.

Varieties of Vitis vinifera form the foundation of the global wine industry, with all familiar wine types produced

from this species Photo: Geetha Paul

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Editorial

by Fr Dr Abraham Mulamoottil

airis4D, Vol.3, No.8, 2025

www.airis4d.com

This edition starts with the article by Arun Aniyan.

Need for AI Regulations: A Layperson’s Guide.

He emphasises the urgent need for comprehensive

regulations to govern the rapidly evolving ﬁeld

of Artiﬁcial Intelligence (AI). While AI holds

immense promise—transforming healthcare, education,

transportation, and climate solutions—it also poses

signiﬁcant risks if left unchecked. Key concerns

include algorithmic bias, privacy invasion, lack of

accountability, job displacement, misinformation, and

the ethical dilemmas surrounding autonomous weapons.

The article argues that just as rules exist for cars and

medicine, AI requires similar guardrails to ensure

safety, fairness, and accountability. Rather than stiﬂing

innovation, thoughtful regulation can guide ethical

development, foster public trust, and ensure AI serves

the collective good.

The article Ambiguity, Redundancy, and

Predictability: An Entropic View of Human

Language by Jinsu Ann Mathew explores how

the concepts of entropy from information theory

help explain three key features of natural

language—ambiguity, redundancy, and predictability.

Though these features may seem contradictory, they

work together to make human communication both

expressive and reliable. Ambiguity introduces

uncertainty and ﬂexibility, increasing entropy but

enriching meaning; redundancy reduces entropy

by reinforcing information, improving clarity and

robustness; and predictability allows eﬃcient

processing by lowering uncertainty in communication.

Rather than ﬂaws, these characteristics are strategic

tools that language uses to balance clarity and creativity.

The article argues that the power of human language

lies not in minimising entropy, but in managing it

intelligently to achieve a dynamic balance between

expressiveness and comprehensibility.

Ajit Kembhavi’s article Black Hole Stories-20:

The LIGO Gravitational Wave Detectors provides

an in-depth yet accessible explanation of how LIGO

(Laser Interferometer Gravitational-Wave Observatory)

detects gravitational waves using advanced Michelson

interferometry. Gravitational waves slightly alter the

distances between mirrors in the interferometer, causing

detectable changes in the interference patterns of

laser beams. LIGO’s detectors—featuring 4 km-long

arms, high-powered stabilised lasers, ultra-reﬂective

mirrors, and extreme vacuum systems—are engineered

to measure minuscule mirror displacements as small

as one-thousandth the diameter of a proton. The

article describes how noise from seismic activity,

thermal eﬀects, and air molecules is minimised using

sophisticated technology, allowing LIGO to achieve the

sensitivity necessary to detect distant cosmic events.

The current version, Advanced LIGO (aLIGO), is most

eﬀective in the 100–300 Hz frequency range and has

already made groundbreaking detections, paving the

way for future discoveries and upcoming facilities like

LIGO-India.

The article Is Every Crash a Firework?

Observing Starbursts in Interacting Galaxies by

Robin Thomas explores how interactions between

galaxies aﬀect star formation, using ultraviolet (UV)

and radio (HI) observations of three nearby galaxy pairs.

While such encounters can trigger dramatic bursts of

star formation, the study ﬁnds that the outcomes vary

widely depending on factors like encounter geometry,

gas content, and especially the mass ratio between

galaxies. Near-equal-mass encounters tend to produce

stronger star formation enhancements, whereas more

unequal pairs show only modest eﬀects. Interestingly,

galaxy separation alone does not correlate well with

star formation activity. The research also identiﬁes

candidates for tidal dwarf galaxies—new galactic

entities forming from stripped material, highlighting

that interactions can both disrupt and create galaxies.

These ﬁndings support a nuanced view of galaxy

evolution, advocating for future studies combining

simulations with molecular gas and spectral data to

better understand how cosmic collisions shape the

universe.

Sindhu G’s article Understanding the Johnson

Magnitude oﬀers a comprehensive overview of the

Johnson photometric system, a foundational method

in observational astronomy for measuring stellar

brightness. Developed in the 1950s by Harold

Johnson and William Morgan, the system introduced

standardised broadband ﬁlters—U (ultraviolet), B

(blue), and V (visual), later expanded to R

(red) and I (infrared)—which remain central to

astrophysical research. The system revolutionised

photometry by enabling accurate, reproducible

brightness measurements and colour indices like (B–V),

which help determine stellar temperatures, classify

stars, correct for interstellar extinction, and estimate

distances. Despite newer systems like SDSS and CCD-

adapted ﬁlters, the Johnson system endures due to

its historical continuity, simplicity, and widespread

calibration legacy. It remains a cornerstone in both

research and education, linking past and present

astronomical observations.

The article Protein Folding and Its Vital

Role in Biology by Geetha Paul explains how

proteins—essential molecules responsible for nearly

all cellular functions—must fold into precise three-

dimensional shapes to function properly. This folding

process begins as a linear chain of amino acids is

assembled during translation and proceeds through

stages, forming secondary structures (e.g., alpha

helices, beta sheets), tertiary structures, and sometimes

quaternary structures. Molecular chaperones facilitate

proper protein folding and prevent errors under stress.

Misfolded proteins can aggregate, leading to serious

diseases like Alzheimer’s, Parkinson’s, and cystic

ﬁbrosis by disrupting cellular function and triggering

toxicity, inﬂammation, and impaired clearance systems.

The article emphasises that understanding protein

folding and misfolding is crucial for advancing

treatments in neurodegenerative and other protein-

related diseases.

iii

Contents

Editorial ii

I Artiﬁcial Intelligence and Machine Learning 1

1 Need for AI Regulations: A Layperson’s Guide 2

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 What Exactly is AI Regulation? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 The Promise of AI: Why We’re Excited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.4 The Perils of Unregulated AI: Why We Should Be Concerned . . . . . . . . . . . . . . . . . . . . 3

1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.6 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2 Ambiguity, Redundancy, and Predictability: An Entropic View of Human Language 7

2.1 Ambiguity: When One Form Has Many Meanings . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2 Redundancy: Saying More to Ensure Understanding . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.3 Predictability: Anticipating What Comes Next . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.4 Striking the Balance: Why Language Needs All Three . . . . . . . . . . . . . . . . . . . . . . . . 9

II Astronomy and Astrophysics 11

1 Black Hole Stories-20

The LIGO Gravitational Wave Detectors 12

1.1 A Simple Gravitational Wave Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

1.2 Laser Interferometric Detectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

1.3 The Advanced LIGO Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2 Is Every Crash a Firework? Observing Starbursts in Interacting Galaxies 17

2.1 Introduction: Galaxies in Motion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.2 Observing in Two Languages: Ultraviolet and Radio . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.3 Trends in the local star formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.4 Global Context: Placement on the Star-Forming Main Sequence . . . . . . . . . . . . . . . . . . 19

2.5 Limitation of the study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.6 Future scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3 Understanding the Johnson Magnitude System in Astronomy 22

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.2 The Historical Background and Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.3 Structure of the Johnson Photometric System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.4 Applications of Johnson Magnitudes in Astrophysics . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.5 Comparison with Other Photometric Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

CONTENTS

3.6 Limitations and Calibration Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.7 Legacy and Continuing Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

III Biosciences 26

1 Protein Folding and its Vital Role in Biological Function 27

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

1.2 Translation and Primary Structure Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

1.3 Folding to Secondary and Tertiary Structure: The Search for Native Conformation . . . . . . . . 28

1.4 Quality Control, Quaternary Structure Assembly, and Functional Veriﬁcation . . . . . . . . . . . 28

1.5 Mechanisms by Which Protein Aggregates Disrupt Cellular Function . . . . . . . . . . . . . . . 29

Part I

Artiﬁcial Intelligence and Machine Learning

Need for AI Regulations: A Layperson’s

Guide

by Arun Aniyan

airis4D, Vol.3, No.8, 2025

www.airis4d.com

1.1 Introduction

Artiﬁcial Intelligence (AI) is rapidly transforming

our world. From the helpful suggestions on our

smartphones to the complex algorithms driving self-

driving cars, AI is no longer a futuristic concept but

an integral part of our daily lives. While AI oﬀers

immense potential for progress and innovation, its

rapid advancement also brings signiﬁcant challenges

and risks. Just as we have rules and laws for new

technologies like cars or medicine, we need a clear set

of guidelines and regulations for AI. This article will

explain, in simple terms, why AI regulations are not just

a good idea but an absolute necessity for our collective

future.

1.2 What Exactly is AI Regulation?

Before we dive into why we need them, let’s

understand what AI regulations are. Think of

regulations as a rulebook. For cars, we have traﬃc

laws, seatbelt regulations, and manufacturing standards

to ensure safety. For medicine, there are strict rules

about testing, approval, and prescription to protect

public health. AI regulations aim to do the same for AI.

Set Standards: Deﬁne what is considered

acceptable and safe AI behaviour.

Establish Responsibilities: Determine who is

accountable when AI makes a mistake or causes

harm.

Protect Rights: Safeguard individual privacy,

prevent discrimination, and ensure fairness.

Foster Trust: Build public conﬁdence in AI

technologies by ensuring they are developed and

used responsibly.

Guide Innovation: Provide a clear framework for

developers and companies, encouraging ethical

and beneﬁcial AI development while preventing

harmful applications.

The burgeoning ﬁeld of artiﬁcial intelligence presents

both immense opportunities and signiﬁcant challenges.

Therefore, the discussion around AI regulations is

not an attempt to hinder technological advancement

or stiﬂe the groundbreaking work being done by

innovators. On the contrary, the core objective of

developing comprehensive AI regulations is to establish

a framework that ensures the beneﬁts of AI are widely

distributed across society, promoting inclusivity and

equitable access to these beneﬁts. Simultaneously,

it’s crucial to proactively mitigate potential negative

consequences, preventing the inadvertent creation of

new societal, ethical, or economic problems that could

arise from the unchecked development and deployment

of AI systems. This thoughtful and balanced approach is

crucial for harnessing the full potential of AI responsibly

and sustainably.

1.3 The Promise of AI: Why We’re Excited

1.3 The Promise of AI: Why We’re

Excited

To understand the need for regulation, it’s

important to appreciate the incredible promise of AI.

AI can:

Revolutionise Healthcare: Assist in diagnosing

diseases earlier, developing personalised

treatments, and even speeding up drug discovery.

Imagine AI helping doctors identify subtle signs

of illness that human eyes might miss.

Transform Transportation: Lead to safer and

more eﬃcient self-driving vehicles, reducing

accidents and traﬃc congestion.

Boost Economic Growth: Automate repetitive

tasks, increase productivity, and create new

industries and job opportunities.

Address Global Challenges: Help us tackle

climate change, optimise energy consumption,

and manage natural resources more eﬀectively.

Enhance Education: Provide personalised

learning experiences for students, adapting to

their individual needs and pace.

With such vast potential, it’s easy to be optimistic. The

future may hold advancements in healthcare, education,

and environmental sustainability, ultimately leading

to a more prosperous and equitable world. Imagine

AI-driven diagnostics that identify diseases at their

earliest stages, personalised learning platforms that

adapt to each student’s unique needs, or intelligent

systems that optimise energy consumption and waste

reduction. These are just a few glimpses of the positive

transformations AI promises.

However, without proper guardrails, this promise

can quickly turn into peril. The very power that allows

AI to solve complex problems also presents signiﬁcant

risks if not managed responsibly. Unregulated AI could

lead to widespread job displacement, algorithmic bias

perpetuating societal inequalities, or even autonomous

systems making critical decisions without human

oversight. The potential for misuse, such as in

surveillance or the spread of misinformation, is also a

serious concern. Therefore, establishing robust ethical

frameworks and regulatory measures is not merely an

option, but an urgent necessity to ensure AI serves

humanity’s best interests.

1.4 The Perils of Unregulated AI:

Why We Should Be Concerned

While the beneﬁts are clear, the risks of

unregulated AI are equally signiﬁcant and, in some

cases, truly alarming.

1.4.1

Bias and Discrimination: The ”Garbage

In, Garbage Out” Problem

AI systems learn from the data they are fed. If this

data reﬂects existing human biases—whether conscious

or unconscious—the AI will learn and perpetuate those

biases. This is often referred to as ”garbage in, garbage

out.”

Some examples are :

Hiring Algorithms: If an AI hiring tool is trained

on historical data where certain demographics

were less likely to be hired for speciﬁc roles, it

might unfairly screen out qualiﬁed candidates

from those groups, even if they are equally or

more capable.

Facial Recognition: Studies have shown that

some facial recognition systems are less accurate

at identifying individuals with darker skin

tones or women, leading to higher rates of

misidentiﬁcation and potential wrongful arrests.

This is because the training data for these systems

was predominantly composed of lighter-skinned

individuals and men.

Loan Approvals: An AI system used by banks

to approve loans could deny loans to individuals

from certain neighbourhoods or backgrounds

if its training data links those demographics

to higher default rates, even if the individual

applicant is creditworthy.

Regulations can mandate fairness audits, require

transparency in data collection, and impose penalties for

discriminatory outcomes, forcing developers to actively

identify and mitigate bias in their AI systems.

1.4 The Perils of Unregulated AI: Why We Should Be Concerned

1.4.2 Privacy Invasion: The All-Seeing Eye

AI systems often require vast amounts of data to

function eﬀectively. This data can include personal

information, online behaviours, location data, and even

biometric details. Without strict privacy regulations,

this data can be misused or exposed.

Targeted Advertising: While often seen as

harmless, extreme personalisation based on AI

analysis of personal data can lead to manipulative

advertising practices or even psychological

targeting.

Surveillance: Governments and corporations

could use AI-powered surveillance systems to

track citizens’ movements, monitor their online

activities, and analyse their behaviours without

consent, leading to a chilling eﬀect on civil

liberties.

Data Breaches: AI systems storing massive

amounts of personal data become prime targets

for cyberattacks. A breach could expose sensitive

information, leading to identity theft, ﬁnancial

fraud, and other serious consequences.

Regulations like GDPR (General Data Protection

Regulation) in Europe are pioneers in this area, giving

individuals more control over their data. Future AI

regulations need to expand on this, ensuring data

minimisation (collecting only what’s necessary), secure

data storage, and strict rules around consent and data

usage.

1.4.3 Accountability and Liability: Who’s to

Blame?

When an AI system causes harm, who is

responsible? Is it the developer who coded the

algorithm, the company that deployed it, or the user who

interacted with it? This question becomes incredibly

complex as AI systems become more autonomous. The

following examples illustrate the case.

Self-Driving Car Accidents: If an autonomous

vehicle causes an accident, is it the car

manufacturer, the software developer, or the

owner of the vehicle who is liable for damages

or injuries?

AI in Healthcare: If an AI diagnostic tool

provides an incorrect diagnosis that leads to

adverse patient outcomes, who is accountable?

The hospital, the AI vendor, or the doctor who

relied on the AI’s output?

Automated Trading Systems: A malfunction

in an AI-powered ﬁnancial trading system could

cause signiﬁcant market disruptions or ﬁnancial

losses. Pinpointing responsibility in such a

complex chain of events is challenging.

Regulations can establish clear frameworks for

accountability, deﬁning legal responsibilities for AI

developers, deployers, and users. This can involve

requiring human oversight, independent audits, and

robust testing before deployment.

1.4.4 Job Displacement and Economic

Inequality: The Automation Dilemma

As AI and automation become more sophisticated,

they will inevitably impact the job market. While

new jobs will be created, many existing jobs may

be automated, potentially leading to widespread job

displacement and exacerbating economic inequality if

not managed carefully.

Manufacturing and Logistics: AI-powered

robots and automated systems can perform tasks

historically done by human workers in factories

and warehouses.

Customer Service: Chatbots and AI assistants

are increasingly handling customer inquiries,

reducing the need for human customer service

representatives.

Data Entry and Clerical Work: Many

administrative tasks can be automated by AI.

While regulations can’t stop technological progress,

they can inﬂuence its trajectory. Governments

can implement policies like retraining programs,

universal basic income (UBI) experiments, or incentives

for companies to invest in job creation alongside

automation. Regulations could also require impact

assessments before large-scale AI deployments to

understand potential societal eﬀects.

1.4 The Perils of Unregulated AI: Why We Should Be Concerned

1.4.5 Algorithmic Manipulation and

Misinformation: The Echo Chamber

Eﬀect

The pervasive inﬂuence of AI algorithms,

particularly those employed by social media platforms,

stems from their fundamental design: to maximise

user engagement. This objective, while seemingly

benign, often leads to the prioritisation of content

that aﬃrms a user’s pre-existing beliefs. This

phenomenon, commonly referred to as the creation

of ”echo chambers,” has profound implications for

individual perception and societal discourse.

Within these algorithmic echo chambers, users

are consistently exposed to information that reinforces

their current viewpoints, while dissenting or alternative

perspectives are systematically ﬁltered out. This

selective exposure can lead to a distorted understanding

of reality, as individuals become less aware of the

complexities and nuances of various issues. The

constant validation of existing beliefs can also foster

an increased sense of certainty and an unwillingness to

engage with opposing arguments.

A signiﬁcant consequence of this algorithmic

design is a heightened susceptibility to misinformation

and manipulation. When users are primarily exposed

to content that aligns with their biases, their critical

thinking skills can be dulled. They may become less

adept at discerning factual information from fabricated

narratives, as the content they consume consistently

validates their existing worldview. This vulnerability

makes individuals more susceptible to propaganda,

conspiracy theories, and other forms of deceptive

content, which can be strategically disseminated within

these echo chambers. The long-term eﬀects include a

polarisation of opinions, a breakdown in civil discourse,

and a diminished capacity for collective problem-

solving.

Political Polarisation: AI algorithms can

reinforce existing political views by showing

users only content that aligns with their ideology,

leading to increased polarisation and reduced

civil discourse.

Spread of Fake News: Malicious actors can

use AI to generate highly convincing fake news

articles, images, and videos (deepfakes), which

can spread rapidly and inﬂuence public opinion,

elections, and even incite violence.

Erosion of Critical Thinking: Constant

exposure to algorithmically curated content

can diminish individuals’ capacity for critical

thinking and discernment between fact and

ﬁction.

Regulations could require greater transparency from

platforms about how their algorithms work, impose

stricter rules on content moderation, and mandate fact-

checking initiatives. They could also hold platforms

accountable for the spread of harmful misinformation.

1.4.6 Autonomous Weapons Systems: The

”Killer Robots” Dilemma

One of the most alarming and ethically fraught

concerns surrounding artiﬁcial intelligence is the

proliferation and development of fully autonomous

weapons systems. These are sophisticated AI-powered

weapons, often colloquially referred to as ”killer robots,”

designed to independently select and engage targets

without human intervention or oversight in the decision-

making process. The very notion of machines making

life-or-death decisions raises profound moral, legal, and

ethical questions, striking at the core of human dignity

and accountability.

The potential implications of killer robots are vast

and terrifying. In a conﬂict scenario, the deployment

of such systems could lead to an accelerated pace of

warfare, reducing the time for human deliberation and

potentially escalating conﬂicts beyond control. There

are also concerns about the potential for unintended

consequences, as the AI’s programming might not fully

account for the complexities of real-world situations,

leading to civilian casualties or misidentiﬁcation of

targets. Furthermore, the absence of a human in

the loop blurs the lines of accountability, making it

diﬃcult to assign responsibility when errors or atrocities

occur. The potential for these weapons to fall into the

wrong hands, or to be used in violation of international

humanitarian law, adds another layer of grave concern

1.5 Conclusion

to an already complex issue.

The following are the main concerns in this regard.

Loss of Human Control: Handing over life-

and-death decisions to machines raises profound

ethical and moral questions

Escalation of Conﬂict: Autonomous weapons

could accelerate conﬂicts and reduce the

threshold for war.

Accountability Gap: If an autonomous weapon

commits a war crime, who is responsible? The

programmer, the manufacturer, or the commander

who deployed it?

Many experts and organisations are calling for AI

regulation to ensure its responsible development and

deployment. This is driven by a number of concerns,

including the potential for job displacement, ethical

dilemmas surrounding autonomous decision-making,

the spread of misinformation, and privacy violations.

Proponents of regulation argue that clear guidelines can

foster public trust, encourage innovation within safe

boundaries, and prevent a ”race to the bottom” where

companies prioritise speed over safety. Additionally,

regulation can address issues of accountability when

AI systems cause harm and ensure equitable access to

the beneﬁts of AI technologies.

1.5 Conclusion

The incredible potential of Artiﬁcial Intelligence

is undeniable, promising advancements that can enrich

our lives and solve complex global challenges. However,

as this guide has outlined, the path to fully realising

these beneﬁts is fraught with signiﬁcant risks if left

unregulated. From the insidious spread of bias and

the erosion of privacy to the complex questions of

accountability and the profound ethical dilemmas posed

by autonomous weapons, the perils of unchecked AI

are too great to ignore.

Just as societies have historically established rules

and frameworks for other transformative technologies, a

comprehensive and proactive approach to AI regulation

is not merely advisable but essential. Such regulations

are not intended to stiﬂe innovation, but rather to guide

it responsibly, ensuring that AI development remains

ethical, transparent, and aligned with human values.

By setting clear standards, deﬁning accountability,

protecting fundamental rights, and mitigating societal

disruptions, well-crafted AI regulations can foster public

trust and pave the way for a future where AI serves

humanity’s best interests, rather than undermining them.

The time to act is now, to ensure that the transformative

power of AI is harnessed for the collective good,

securing a safer and more equitable future for all.

1.6 References

https://gdpr-info.eu/

https://www.brookings.edu/articles/algorithmic-

bias-how-data-discriminates/

https://www.un.org/disarmament/topics/lethal-

autonomous-weapons-systems/

https://digital-strategy.ec.europa.eu/en/policies/ethical-

guidelines-trustworthy-ai

About the Author

Dr.Arun Aniyan is leading the R&D for

Artiﬁcial intelligence at DeepAlert Ltd,UK. He comes

from an academic background and has experience

in designing machine learning products for diﬀerent

domains. His major interest is knowledge representation

and computer vision.

Ambiguity, Redundancy, and Predictability:

An Entropic View of Human Language

by Jinsu Ann Mathew

airis4D, Vol.3, No.8, 2025

www.airis4d.com

In the previous articles, we explored how entropy

helps quantify uncertainty in language—from the

fundamental deﬁnitions in information theory to speciﬁc

measures like lexical entropy, n-gram entropy, and

cross-entropy in language models. Building on that

foundation, we now turn to three essential characteristics

of natural language: ambiguity, redundancy, and

predictability.

These features often seem contradictory.

Ambiguity introduces multiple meanings, which could

confuse a listener. Redundancy repeats information

that might appear unnecessary. Predictability makes

language easier to process but potentially less

informative. Yet, together, these features form a delicate

balance that allows human communication to be both

expressive and reliable.

Viewed through the lens of entropy, we can

better understand why language behaves this way.

Ambiguity reﬂects areas of high entropy—where

multiple interpretations are possible. Redundancy

works in the opposite direction, lowering entropy

to make messages more robust against noise or

misunderstanding. Predictability, meanwhile, allows

both humans and machines to process information

eﬃciently by reducing uncertainty in upcoming words

or phrases.

This article explores how entropy provides a useful

framework for analyzing these three aspects of language.

We’ll see how ambiguity can be measured using lexical

entropy, how redundancy contributes to communication

reliability, and how predictability shapes both sentence

processing and model performance. Rather than treating

these features as limitations, we’ll understand them as

essential design elements of human language—and key

considerations in computational models that aim to

understand or generate text.

2.1 Ambiguity: When One Form Has

Many Meanings

Ambiguity occurs when a word, phrase, or

sentence can be interpreted in more than one way.

It adds uncertainty to communication and is a key

contributor to higher entropy in language. There are

several forms:

Lexical ambiguity

A single word has multiple meanings.

Example: “He swung the bat.”

→

The word “bat”

could refer to an animal (a ﬂying mammal) or a piece

of sports equipment. Without more context, it’s unclear

which one is meant.

Syntactic ambiguity

A sentence allows multiple grammatical

interpretations.

Example: “Visiting relatives can be annoying.”

→

This can mean either (1) the act of visiting relatives

is annoying, or (2) the relatives who are visiting are

annoying.

2.2 Redundancy: Saying More to Ensure Understanding

Semantic ambiguity

The overall meaning is unclear even if the grammar

is correct.

Example: “He saw the man with the telescope.”

→

It’s unclear whether he used a telescope to see the

man, or whether the man had the telescope.

In all these cases, ambiguity increases

uncertainty—and therefore entropy—because multiple

interpretations are possible. However, in real

communication, context usually helps resolve the

intended meaning. For example, in “The bat ﬂew

across the cave,” the context makes it clear that “bat”

refers to the animal.

In computational linguistics, this kind of

uncertainty is often measured using lexical entropy.

Words that are used evenly across multiple senses tend

to have higher entropy, indicating greater ambiguity.

Systems like search engines or translation tools must

estimate the most likely meaning based on surrounding

words.

2.2 Redundancy: Saying More to

Ensure Understanding

Redundancy in language refers to the inclusion of

extra information that may not be strictly necessary

to convey the core message, but helps make

communication more clear, reliable, and error-resistant.

From an information theory perspective, redundancy

works by reducing entropy—it makes language more

predictable and easier to process, especially when

there’s a chance of miscommunication.

While redundancy might seem wasteful, it’s

actually a key feature of natural language. It helps

listeners understand a message even when parts of it

are unclear, misheard, or missing. This is particularly

useful in noisy environments, fast speech, or casual

conversation.

Consider this sentence:

“I saw it with my own eyes.”

→

The phrase “with

my own eyes” is technically unnecessary—“I saw it”

already conveys the full meaning. But the repetition

serves to emphasize the speaker’s certainty and make

the message more forceful and clear. If the sentence

were spoken in a noisy room, the added redundancy

also increases the chance that the core meaning is still

understood.

Another example:

“The reason he left is because he was tired.”

→

This sentence contains overlapping phrases: “the

reason” and “because” both express causality. One

could be removed without changing the basic meaning,

but including both makes the structure more familiar

and easier to follow.

Redundancy is a natural strategy for error

correction in human communication. Just like in

digital systems, where repeated signals can help correct

transmission errors, redundant elements in language

help listeners recover meaning when the input is

incomplete or distorted.

In short, redundancy may reduce eﬃciency, but it

improves clarity, emphasis, and robustness—qualities

that are essential in real-world communication.

2.3

Predictability: Anticipating What

Comes Next

Predictability in language refers to how easily a

listener or reader can guess what word or phrase is likely

to come next. It plays a central role in how we process

and understand language in real time. When a sentence

follows familiar patterns or uses common phrases, it

becomes more predictable and easier to comprehend.

From an information theory perspective,

predictability is closely related to low entropy. The

more predictable a word is in its context, the lower its

entropy. This means the listener needs less eﬀort to

understand it, since it causes less surprise.

For example:

“She drank a cup of...”

→

Most people would expect the next word to

be something like “tea” or “coﬀee.” These are high-

probability continuations in everyday language, so they

are highly predictable and carry less information. If

the sentence ended with “vinegar” instead, it would be

unexpected, and therefore more informative—but also

2.4 Striking the Balance: Why Language Needs All Three

more surprising.

Predictability is not just a linguistic curiosity—it’s

central to how our brains handle language. Studies

in psycholinguistics show that people read predictable

words faster, ﬁxate on them for less time, and remember

them diﬀerently. Our minds are constantly generating

expectations based on what we’ve heard or read so far.

Language models work in a similar way. They

calculate the probability of each possible next word

and use that to generate ﬂuent sentences. The more

predictable a word is in its context, the higher its

probability—and the lower its contribution to the

model’s calculated entropy.

In practice, predictability helps make

communication eﬃcient and smooth. Speakers

can rely on shared knowledge and familiar structures,

while listeners use context to ﬁll in gaps. This

makes language faster to process and easier to follow,

especially in everyday conversation.

2.4 Striking the Balance: Why

Language Needs All Three

Language is not designed to eliminate

entropy—but to manage it intelligently. Ambiguity,

redundancy, and predictability each aﬀect the level

of entropy in diﬀerent ways, and the eﬀectiveness of

language lies in how it balances these forces rather than

avoiding them entirely.

Ambiguity increases entropy—on purpose

Ambiguity introduces uncertainty into a message,

raising entropy. A word with multiple meanings

or a sentence open to multiple interpretations can

make communication less certain on the surface.

But this isn’t a ﬂaw—it’s a feature. Ambiguity

makes language compact and ﬂexible, allowing us

to reuse words and structures in diﬀerent contexts.

It enables metaphor, humor, creativity, and even

politeness.Without ambiguity, language would be rigid

and unnecessarily long. But without limits, it would

become unintelligible.

Redundancy reduces entropy—strategically

Redundancy helps bring entropy down by

repeating or reinforcing information. This makes

messages more robust and recoverable, especially

in noisy or uncertain environments. From a

communication theory standpoint, redundancy serves

as a buﬀer against loss: even if part of the signal is lost,

the message can still be understood. Redundancy lowers

entropy—but at the cost of eﬃciency. So language uses

it selectively, where precision and clarity matter most.

Predictability controls entropy—eﬃciently

Predictable patterns in language reduce entropy by

making it easier to anticipate the next word or structure.

This speeds up processing and lightens the cognitive

load. Language relies on familiar phrases, grammar

rules, and contextual cues so that not every word needs to

be consciously analyzed. High predictability means low

entropy—but too much predictability makes language

dull or repetitive.

The Real Power: Balancing Entropy, Not

Erasing It

The genius of human language is in how it balances

entropy, rather than trying to minimize or maximize it

outright.

A poem may allow more ambiguity (higher

entropy) for emotional or aesthetic depth.

A legal document may use redundancy (lower

entropy) to avoid misinterpretation.

Everyday conversation often favors predictability

to enable fast and ﬂuent exchange.

Natural language operates in this sweet spot—not

too chaotic, not too rigid. It allows just enough entropy

to be expressive, but controls it through redundancy

and predictability to remain clear and usable.

References

Maximum Entropy Models For Natural Language

Ambiguity Resolution

Entropy, prediction and the cultural ecosystem of

human cognition

2.4 Striking the Balance: Why Language Needs All Three

The Word Entropy of Natural Languages

Prediction during language comprehension: what

is next?

The communicative function of ambiguity in

language

Excess entropy in natural language: Present state

and perspectives

Redundancy can beneﬁt learning: Evidence from

word order and case marking

About the Author

Jinsu Ann Mathew is a research scholar

in Natural Language Processing and Chemical

Informatics. Her interests include applying basic

scientiﬁc research on computational linguistics,

practical applications of human language technology,

and interdisciplinary work in computational physics.

Part II

Astronomy and Astrophysics

Black Hole Stories-20

The LIGO Gravitational Wave Detectors

by Ajit Kembhavi

airis4D, Vol.3, No.8, 2025

www.airis4d.com

In this story we will describe the LIGO

gravitational wave detectors, which are based on the

principle of the Michaelson interferometer.

1.1 A Simple Gravitational Wave

Detector

We have seen in BHS-19 how the concept

of a Michaelson interferometer provides a way to

detect gravitational waves. We reproduce here, for

convenience, the corresponding ﬁgure.

Figure 1: A schematic representation of a Michaelson

interferometer for detecting gravitational waves.

Credit: Kaushal Sharma and Ajith Parameswaran.

The functioning of the interferometer has been

described in BHS-19. To summarise, half of a laser

beam reaching the semi-reﬂecting mirror M passes

through to mirror M1, while the other half is reﬂected

to mirror M2. The returning beams meet at the detector.

The distances D1 and D2 are equal, so the beams, which

start from a single source, reach the detector in phase

and combine to produce a bright spot at the centre of

the detector. Now suppose a gravitational wave passes

through the circle of particles, so that it is stretched to

form an ellipse as shown on the right of the ﬁgure. The

mirror M1 is now farther from M than it was in the

absence of the wave, while M2 has moved closer to M, so

D2 is greater than D1. The distances travelled by the two

beams are therefore diﬀerent and for a certain diﬀerence

in the path length, equal to an odd integral multiple

of half the wavelength of the laser, the interference is

destructive and a dark spot is seen. As the mirrors move

back and forth the interference changes periodically,

which can be detected by the changing intensity of the

spot. The changing interference becomes a signature

of the passage of a gravitational wave. The idea is

schematically represented in the lower half of Fig. 1.

1.2 Laser Interferometric Detectors

These are based on the principle of the Michelson

interferometer described above. To be able to detect the

very weak gravitational waves reaching the Earth from

distant cosmic events, the detectors have very long arm

lengths. The present detectors of this type are (1) the

detectors near Livingston, Louisiana and near Hanford,

Washington State, which are both part of LIGO, the

Laser Interferometric Gravitational Wave Observatory

– each with 4 km long arms; (2) the VIRGO detector

near Pisa with 3 km arms; (3) the GEO600 detector

1.3 The Advanced LIGO Detector

near Hanover with 600 m arms and (4) the KAGRA

underground detector at Kamioka in Japan with 3 km

long arms. The detectors undergo periodic upgrades in

technology to make them more sensitive. The current

version of the LIGO detector is known as Advanced-

LIGO (aLIGO), and the following description pertains

to this version of the detector.

Translating from laboratory scale interferometers

to the LIGO scale with each arm being 4 km long

is obviously rather diﬃcult. The diﬃculty is greatly

compounded by the accuracy that is required for the

detection of gravitational waves. In the laboratory,

an interferometer is used, for example, to measure

the wavelength of light. This requires an accuracy

of about

−12

m in very precise measurements. But

the displacement caused by a gravitational wave in the

LIGO mirrors can be as small as

−19

m, which is about

8500

of the radius of a proton. This extremely small

displacement has to be observed against a background

of much larger displacements caused by various eﬀects.

The required sensitivity is reached by adapting a

series of technical innovations designed to eliminate

or minimise spurious vibrations, which we can term as

noise. Some of the measures are (1) using a powerful

laser as the source of the light beams; (2) using extra

mirrors to increase the intensity of light and the distance

traversed by the beam through repeated reﬂections; (3)

placing the entire interferometer in a high vacuum

enclosure and (4) using very sophisticated suspensions

for holding the mirrors. The details that we describe

below are for aLIGO, but similar systems are used in

the other detectors.

1.3 The Advanced LIGO Detector

Michaelson interferometers originally used

conventional light at optical wavelengths, whereas

gravitational wave detectors use lasers. The laser

provides a very intense, steady and non-diverging

beam of light at a precisely determined wavelength.

The complex laser device used in the aLIGO detector

produces a laser beam at a wavelength of 1064 nm, with

output power of 200 Watts. It is one of the most steady

and powerful lasers operating at the chosen wavelength,

which is in the near-infrared part of the spectrum. The

laser is stabilised to a level of one part in ten billion

in intensity and one part in a billion in frequency.

The power produced by the laser needs further great

ampliﬁcation to serve the needs of the detector, which

is achieved by recycling the beam between mirrors

designed for the purpose. The power ﬁnally achieved is

about 750 kilo Watts.

The length of each arm of LIGO, from the beam

splitter to the reﬂecting mirror at the end of the arm is 4

km. The longer this length, the higher is the sensitivity

of the detector. A much longer length is eﬀectively

achieved by placing an additional mirror, called a signal

recycling mirror, in each arm exactly 4 km from the

mirror at the end of the arm. The 4 km long space is

known as a Fabry-Perot cavity. The beam in each cavity

is reﬂected back and forth about 300 times, eﬀectively

increasing the arm length to about 1200 km, and also

increasing the light intensity in the arms, which helps

in reducing noise.

The arrangement of the optics is such that

when the arm lengths are equal, a dark spot is

produced at the detector. As the arm lengths change,

the interference changes, and the spot gets brighter.

Conventionally, equal arm lengths would lead to

constructive interference and a bright spot would be

seen at the detector. But in the LIGO interferometer, a

dark spot is chosen by adjusting the optics, because it

is easier to judge the brightening of a dark spot, rather

than the dimming of a bright spot. A schematic of

the aLIGO interferometer is shown in Figure 2. The

functions of the various mirrors are indicated in the

ﬁgure. The arrangement in the real interferometer is far

more complex, of course.

The mirrors in the LIGO interferometer are used

to set the beam paths as described above, but they also

have another crucial function: they act as free particles

which respond to the passage of a gravitational wave.

We have seen in BHS-19 how a circle of free particles

oscillates between circular and elliptical forms as a

gravitational wave passes through. In the gravitational

wave interferometer, the two mirrors at the end of the

beam paths (M1 and M2 in Figure 1, and marked as

end mirrors in Figure 2) can be considered to be two

1.3 The Advanced LIGO Detector

Figure 2: A schematic diagram of the Advanced LIGO

interferometer.

Image Credit: D.V. Martynov et al. Phys.Rev.D 93, 112004, 2016.

free particles on a circle. The distance of these mirrors

from the centre is equal, but becomes diﬀerent as a

gravitational wave passes through. For this to happen,

the mirrors are to be free, in the sense that no net

force, other than the gravitational force produced by

the wave are acting on them. If such forces exist, they

would dominate the extremely tiny eﬀect produced. The

mirrors are suspended from above, so that in the vertical

direction the downwards gravitational force of the Earth

is balanced by the upwards tension in the suspension.

The mirrors can move freely in the horizontal direction

and the eﬀect of a gravitational wave would be to change

the horizontal distances.

It is necessary to minimise the eﬀects of any other

forces which would result in the unwanted horizontal

movement of the mirrors, which is considered to

be noise, since it can conceal the tiny eﬀect due to

gravitational waves. That requires advanced mirror

isolation technology, which has evolved over the years.

In spite of the advanced technology used for noise

reduction, there is residual noise which can cause small

displacements in the mirror. There is seismic noise due

to ground vibrations or seismic disturbance caused by

human activities, winds, tidal motions in the ground

caused by the Sun and the Moon similar to tides in the

oceans, and ocean waves which can cause disturbance

even when the coastline is far away. Disturbances can

also be caused due to slight heating by the laser beams

of the mirrors and their suspensions and mechanical loss

of the reﬂective coatings of the mirrors. This aﬀects

the random motion of the particles in them, leading to

thermal noise. The eﬀect of these sources of noise on

the sensitivity of the aLIGO detector has to be taken

into account.

The mirrors which constitute the free particles

have a diametre of 34 cm, thickness 20 cm and weigh

40 kg each, the large mass helping to hold them still.

They are made of extremely pure and homogeneous

fused silica. The mirrors have very precisely deﬁned

shapes which are accurate to better than

−12

m, which

is far greater precision than is required for mirrors used

in large optical telescopes. The surfaces of the mirrors

are so highly reﬂective that they absorb only about one

out of 2.3 million photons which reach the mirrors. Any

absorbed photons lead to heating of the mirror. Due to

the very high intensity of the light, in spite of the small

absorption, the heat produced is suﬃcient to produce a

slight distortion of the mirror surface. To avoid that, a

system is used, which monitors the shape of the surface

and makes the necessary corrections.

The entire LIGO interferometer is operated in

a high vacuum. The laser beams pass through two

steel tubes each of which is 4 km long and the mirrors

are enclosed by end stations. The whole evacuated

assembly has a volume of about 10 million litres. The

only bigger evacuated volume than LIGO is the Large

Hadron Collider in CERN in Switzerland, which was

used to discover the Higgs Boson. The vacuum is

so good that the pressure of the residual gases in the

volume is only a trillionth of the air pressure at sea level.

The high vacuum is required for various reasons. The

molecules of air are in incessant motion due to their

heat energy. The molecules collide with the mirrors

to produce tiny motions in them. If the pressure is not

low enough, the jitter produced in the mirrors would

produce noise at an unacceptable level. When light

propagates through air, its direction of travel can be

slightly altered depending on the density of the air.

There are always small disturbances in air which can

cause the beam to randomly deviate from its straight

path, which changes slightly the length travelled by the

beam, adversely aﬀecting the interference pattern. The

air can contain dust particles which scatter the beam

1.3 The Advanced LIGO Detector

Figure 3: The sensitivity of the aLIGO detectors as

function of frequency. See text for a description of the

ﬁgure.

Image Credit; B. P. Abbott et al. Phys. Rev. Letters 116, 131103, 2016.

from its straight line path. These eﬀects are all very

small and would not matter at all in other situations.

But because of the very small changes produced by

gravitational waves the eﬀects can become important in

comparison. The very high vacuum is needed to reduce

such eﬀects to an acceptable level.

The sensitivity of the aLIGO detector to

gravitational waves and to noise in the system as a

function of the gravitational wave frequency is shown

in Fig. 3. The frequency at which the measurement is

made is shown on the horizontal axis, while a quantity

known as the strain noise is shown on the vertical

axis. This quantity is indicative of the ratio of the

net displacement produced in the two arms to the total

distance traversed by the beam in the multiple reﬂections

discussed above. The curve in dark red shows the strain

noise for the aLIGO detector at Hanford during the

ﬁrst observing run O1 in 2015 during which the ﬁrst

detection of a gravitational wave source was made by

aLIGO. The detection will be described in the next story.

The curve in light red is for the detector at Livingston.

The near coincidence of the two curves shows that the

sensitivity of the two detectors has nearly the same

dependence on the frequency.

The green curve shows the sensitivity of an earlier

version of the LIGO detector, while the blue and cyan

curves are the planned sensitivities of future versions

of the detector. A gravitational wave source can be

detected by aLIGO only if the strain produced by the

source is above the curves. How much conﬁdence we

Figure 4: The LIGO observatory in Hanford,

Washington State, USA. The two 4 km long arms are

seen. The central part of the detector is in the buildings

at the intersection of the two arms.

can place in the detection depends upon how much above

the curve the source is located. Therefore, the lower a

curve is on the diagram, the better the detector would

be for observing faint sources. Using this criterion, it

is seen that the aLIGO detectors are most sensitive in

the frequency range of 100-300 Hz. The strong lines at

various points along the red and green curves are due to

speciﬁc contributions to the noise from the suspensions,

from AC power lines and due to signals injected for

calibration purposes. The lines are accounted for in the

analysis of the data.

An aerial view of the LIGO facility at Hanford in

Washington State and a close-up of its Northern arm

are shown in Figure 4. The LIGO-India facility, to be

completed over the next several years, will be similar

in nature.

Further detail about the LIGO detectors and

observatory, and LIGO-India, can be found in the book

Gravitational Waves – A New Window to the Universe,

by Ajit Kembhavi & Pushpa Khare, Springer 2020.

In the next few stories we will describe some of the

gravitational wave sources detected by LIGO, all but

one of which were black hole binaries which merged

together to form a single black hole. The one exception

was a neutron star binary which again merged to form a

black hole.

I thank Dr. Joe Jacob for careful reading of the

story and suggesting corrections.

1.3 The Advanced LIGO Detector

About the Author

Professor Ajit Kembhavi is an emeritus

Professor at Inter University Centre for Astronomy

and Astrophysics and is also the Principal Investigator

of the Pune Knowledge Cluster. He was the former

director of Inter University Centre for Astronomy and

Astrophysics (IUCAA), Pune, and the International

Astronomical Union vice president. In collaboration

with IUCAA, he pioneered astronomy outreach

activities from the late 80s to promote astronomy

research in Indian universities.

Is Every Crash a Firework? Observing

Starbursts in Interacting Galaxies

by Robin Thomas

airis4D, Vol.3, No.8, 2025

www.airis4d.com

2.1 Introduction: Galaxies in Motion

Galaxies evolve within a constantly changing

gravitational landscape. When two massive galaxies

pass suﬃciently close to one another, mutual tidal

forces torque their stellar disks and, more importantly,

redistribute their gaseous reservoirs. Such interactions

can channel gas toward galactic centres, ignite nuclear

starbursts, or expel material into tidal bridges and tails

where it may cool and condense into oﬀ-disk star-

forming regions. However, observational surveys and

simulations increasingly demonstrate that the outcome

is not uniform: while some encounters trigger dramatic,

system-wide increases in star formation, others lead

to only modest enhancements—or even temporary

suppression—depending on encounter geometry, gas

fraction, orbital parameters, and, critically, the mass

ratio of the pair. Our study was designed to quantify this

diversity by examining both the local (kiloparsec-scale)

and global (galaxy-integrated) star-formation response

in a small but well-characterised sample of nearby

interacting systems.

To explore how diﬀerent dynamical conﬁgurations

inﬂuence star formation, we targeted three nearby, nearly

face-on interacting pairs: NGC 2207/IC 2163, NGC

4017/4016 (ARP 305), and NGC 7753/7752 (ARP

86). These systems span a range of interaction stages

and morphologies—from near-grazing encounters

with pronounced ocular features to bridge–satellite

conﬁgurations—allowing us to isolate the role of

geometry and timing. Their proximity permits high

Figure 1: Colour composite images from the HiPS

survey and corresponding UVIT image for the sample

of galaxies are shown in the top and bottom panels,

respectively. The galaxy pairs in our sample are labelled

in panels (a), (b) and (c). The emission in FUV1 for

NGC 2207/IC 2163 and NGC 4016/4107 and in FUV2

for NGC 7752/7753 are shown in panels (d), (e) and

(f), respectively. The red bars in the bottom panels

represent an extent of 20 kpc at the distance to the

galaxy pairs. The foreground stars in the FOV were

conﬁrmed and removed using Gaia DR3 catalogue

(Gaia Collaboration et al., 2021).

spatial resolution in the ultraviolet (UV), enabling us

to resolve individual star-forming clumps and compare

them directly with the underlying gas distribution

traced by neutral hydrogen (HI). By studying diﬀerent

“snapshots” of the interaction sequence, we can extract

trends that would be obscured in a heterogeneous, poorly

resolved sample.

2.2 Observing in Two Languages: Ultraviolet and Radio

2.2 Observing in Two Languages:

Ultraviolet and Radio

Young, massive stellar populations emit strongly

in the far- and near-ultraviolet, making UV imaging an

excellent proxy for recent (

≲

100 Myr) star formation.

We exploited AstroSat/UVIT’s sub-arcsecond point

spread function (

∼ 1.2 − 1.4”

) to isolate discrete star-

forming knots across disks, bridges, and tidal debris.

After standard CCDLAB reduction and photometric

calibration using established zero-points, we derived

star formation rates (SFRs) and surface densities (

SFR

)

for each knot. To map the fuel reservoir and dynamical

response of the gas, we combined these UV maps with

archival or newly reduced HI 21

cm data from the VLA

and GMRT. Neutral hydrogen is both spatially extended

and dynamically sensitive to tidal perturbations; thus, its

column-density distribution provides a direct measure

of where gas has been compressed or displaced. The

juxtaposition of UV and HI maps therefore reveals not

only where stars are forming, but also how eﬃciently

the disturbed gas is being converted into stars.

2.3 Trends in the local star formation

2.3.1 NGC 2207/IC 2163: Triggered Star

Formation and a New TDG Candidate

In NGC 2207/IC 2163, the UVIT data reveal

a broad range of

SFR

values (roughly

−3

−2

⊙

−1

kpc

−2

) distributed both along the primary

interaction interface and on the far side of the main

disk, indicating that tidal perturbations propagate across

the system rather than being conﬁned to the direct

overlap region. A particularly notable result is the

ﬁrst detection of ongoing star formation within the

northwestern gas complex N2207

NW1. Its coincident

HI column density and systemic velocity make it a

strong candidate for a tidal dwarf galaxy (TDG), a

self-gravitating system formed from tidally stripped,

chemically enriched gas rather than from primordial

collapse. This detection underscores the capacity of

interactions not merely to rearrange existing stellar

material, but to seed the formation of entirely new

galactic entities in debris ﬁelds.

2.3.2

ARP 305 (NGC 4017/4016): Distributed,

Moderate Enhancement

The ARP 305 system exhibits widespread but

moderate star formation spread over spiral arms, tidal

debris, and bridges. The spatial distribution suggests

a signiﬁcant prior pericentric passage that stirred the

gas on large scales yet did not funnel it into a single

dominant nuclear starburst. Instead, the interaction

appears to have created numerous local pockets of

compression suitable for star formation. This pattern is

consistent with scenarios in which the strongest tidal

torques have subsided, leaving behind a “fossil record”

of triggered clumps that continue to form stars as long

as suﬃcient gas remains locally unstable.

2.3.3 ARP 86 (NGC 7753/7752): A Gas-Rich

Satellite and Another Debris-Born

Candidate

In ARP 86, the smaller companion NGC 7752

is exceptionally gas-rich, with an HI-to-total mass

fraction around 0.17, which is substantially higher

than typical values reported for classical starbursts.

UV imaging shows moderate to high

SFR

within

the satellite, whereas the main galaxy and connecting

bridge exhibit more modest levels. Embedded

within an HI bridge of column density exceeding

−2

lies 2MASXJ23470758+2926531, which

also hosts detectable star formation. Its location and

properties make it another compelling TDG candidate.

Collectively, these ﬁndings illustrate how tidal bridges

can serve as nurseries for new dwarf systems, provided

that the displaced gas remains suﬃciently dense and

self-gravitating.

The identiﬁcation of active star formation in

debris-bound condensations such as N2207

NW1 and

2MASXJ23470758+2926531 strengthens the evidence

that interactions can spawn long-lived, self-gravitating

tidal dwarf galaxies. TDGs inherit chemically enriched

gas from their progenitors. Their prevalence has

implications for the metallicity distribution in group

environments and the census of dwarf populations

2.4 Global Context: Placement on the Star-Forming Main Sequence

9.0 9.5 10.0 10.5 11.0 11.5

Stellar Mass (M )

2.5

2.0

1.5

1.0

0.5

0.0

0.5

1.0

1.5

SFR (M yr

)

NGC 2207 NGC 4017 NGC 7753

2.0

1.5

1.0

0.5

log(M

)

Figure 2: The SFMS of galaxies is presented. Circles

represent the xCOLD GASS sample (Saintonge et al.,

2017), color mapped to represent the molecular gas

fraction in them. The main sequence obtained from

Dave (2008) is represented by blue dotted lines, with the

red dotted line representing 0.3 dex scatter. Our sample

of main galaxies are also color mapped to represent the

gas fraction. Due to the absence of relevant estimations

of molecular gas mass in NGC 7753, its molecular gas

fraction is not represented. We observe an enhancement

in the global SFR in our sample of main galaxies.

in the nearby universe. By identifying ongoing star

formation in these structures, our work adds to the

growing recognition that galaxy interactions can both

dismantle and create galaxies in the same event.

2.4

Global Context: Placement on the

Star-Forming Main Sequence

To assess the global impact of these interactions,

we located each primary galaxy on the star-forming

main sequence (SFMS), the empirical SFR–

⊙

relation

for star-forming disks. Using literature stellar masses

and SFRs from xCOLDGASS (Saintonge et al.,

2017), we ﬁnd that all three main galaxies lie above

the canonical SFMS (e.g., Dave (2008)), indicating

statistically signiﬁcant but moderate enhancements in

their integrated SFRs relative to isolated counterparts.

Such moderate oﬀsets are increasingly recognised as

characteristic of many interacting systems, highlighting

the need to move beyond a binary “starburst vs.

quiescent” framework when interpreting galaxy-galaxy

encounters.

To quantify the physical parameters that might

govern the observed enhancement, we study the star

formation enhancement as functions of stellar pair mass

ratio and pair separations.

2.4.1 Pair mass ratio as a metric

By quantifying SFR enhancement as the ratio of

each galaxy’s SFR to that of a mass, morphology and

distance matched control sample, we isolate the eﬀect

of interaction parameters. A clear inverse correlation

emerges between enhancement and pair mass ratio:

encounters between near-equal-mass galaxies maximise

tidal torques and gas inﬂows, whereas highly unequal

pairs often generate only weak responses in the more

massive member. This empirical result corroborates

hydrodynamic simulations (e.g., Cox et al., 2008;

Hani et al., 2020), which predict that mass symmetry

enhances dissipation and central gas accumulation.

Consequently, mass ratio should be treated as a ﬁrst-

order predictor when estimating the star-forming impact

of a given interaction.

2.4.2 Pair separation as a limiting parameter

In contrast, projected pair separation shows no

statistically signiﬁcant linear correlation with SFR

enhancement (Pearson r

≈

0.05) across our systems

and in the larger comparison sample. While there

is an obvious physical limit—galaxies too widely

separated cannot tidally inﬂuence one another—the

absence of a tight trend within interacting pairs implies

that separation alone cannot predict the magnitude

of the response. Projection eﬀects, diﬀering orbital

phases, encounter velocities, and time lags between

gas compression and observable star formation all

contribute to the scatter. Thus, separation should be

interpreted as a contextual parameter, not a deterministic

one.

2.5 Limitation of the study

Several caveats accompany our analysis. Projected

separations may underestimate true three-dimensional

distances, diluting any intrinsic trends with tidal

strength. UV-based SFRs are sensitive to dust

attenuation and recent star-formation histories. Even

though the UV ﬂuxes were accounted for extinction,

2.6 Future scope

Figure 3: (Left) SFR enhancement studied as a function of the pair mass ratios. We ﬁnd an inverse correlation

between the SFR enhancement and mass ratios. (Right) SFR enhancement was studied as a function of the pair

separation. Star formation enhancement for interacting galaxies from Knapen et al. (2015) are also plotted as a

function of pair separation. We ﬁnd an absence of correlation, with a Pearson coeﬃcient of 0.05.

residual uncertainties can remain without full spectral

energy distribution ﬁtting. Finally, although our three

systems are illustrative, they cannot capture the full

diversity of interaction outcomes—hence the necessity

of the larger statistical cross-check.

2.6 Future scope

A natural next step is to couple our observational

constraints with tailored N-body/hydrodynamical

simulations that recover each system’s orbital history

and gas inﬂow patterns. High-resolution CO mapping

with ALMA or NOEMA would directly link dense

molecular gas to the UV-bright knots, clarifying

the eﬃciency of the

HI − H

conversion into stars

disturbed environments. Integral-ﬁeld spectroscopy

can disentangle photoionization from shock excitation

in extraplanar regions and bridges, providing a more

complete diagnostic of the physical processes at work.

Finally, deeper, wider-ﬁeld UV and radio observations

will reveal fainter tidal structures and nascent TDGs,

enabling a more complete census of interaction-induced

substructure.

2.7 Conclusion

Galaxy–galaxy interactions inﬂuence the

morphology, gas dynamics and star formation

properties, all of which depend sensitively on the

physical context of each encounter. In the three

nearby pairs examined here, we ﬁnd that tidal forces

redistribute gas and ignite patchy, kiloparsec-scale

bursts of star formation across disks, bridges, and

debris, while the galaxies’ integrated SFR rise only

moderately above the star-forming main sequence.

Among the parameters we probed, the stellar

mass ratio emerges as the most predictive lever:

encounters between near-equal-mass partners yield

stronger tidal torques, deeper gas inﬂows, and larger

SFR enhancements than highly unequal pairs. By

contrast, present-day projected separation shows no

tight correlation with enhancement, underscoring that

proximity alone is a blunt proxy once a system is

already interacting. These empirical trends corroborate

hydrodynamic simulations and argue for including mass

symmetry, orbital geometry, and gas fraction as ﬁrst-

order inputs in models of interaction-triggered activity.

Taken together, these results advocate for a multi-

scale, multi-phase approach to interaction studies.

Future work that combines tailored simulations with

molecular gas and integral-ﬁeld spectroscopy will be

2.7 Conclusion

essential to reconstruct the dynamical histories of such

systems and to quantify, with greater physical ﬁdelity,

how often and how eﬃciently interactions reshape the

star-forming lives of galaxies.

Correspondence: robinthomas546@gmail.com

Reference: Thomas et al. (2024), MNRAS, 534, 1902

Data Sources: AstroSat UVIT; VLA; uGMRT

References

Cox T. J., Jonsson P., Somerville R. S., Primack J.

R., Dekel A., 2008, , 384, 386

Dave R., 2008, , 385, 147

Gaia Collaboration et al., 2021, , 649, A1

Hani M. H., Gosain H., Ellison S. L., Patton D. R.,

Torrey P., 2020, , 493, 3716

Knapen J. H., Cisternas M., Querejeta M., 2015, , 454,

1742

Saintonge A., et al., 2017, , 233, 22

About the Author

Dr Robin is currently a Project Scientist at the

Indian Institute of Technology Kanpur. He completed his

PhD in astrophysics at CHRIST University, Bangalore,

with a focus on the evolution of galaxies. With a

background in both observational and simulation-based

astronomy, he brings a multidisciplinary approach to his

research. He has been a core member of CosmicVarta,

a science communication platform led by PhD scholars,

since its inception. Through this initiative, he has actively

contributed to making astronomy research accessible to

the general public.

Understanding the Johnson Magnitude

System in Astronomy

by Sindhu G

airis4D, Vol.3, No.8, 2025

www.airis4d.com

3.1 Introduction

In the ﬁeld of observational astronomy, the precise

measurement of stellar brightness is fundamental to

our understanding of the universe. From determining

stellar distances and compositions to studying the

evolution of galaxies, photometric measurements

underpin nearly every branch of astrophysical research.

Over the years, various magnitude systems have been

developed to quantify the brightness of stars and other

celestial objects. Among these, the Johnson-Morgan

photometric system, more commonly referred to as the

Johnson system, stands as one of the most inﬂuential

and widely adopted standards.

Developed in the early 1950s by Harold Lester

Johnson and William Wilson Morgan, the Johnson

system was the ﬁrst comprehensive photoelectric

photometric system. It replaced older visual methods

with more accurate and reproducible measurements

across well-deﬁned ﬁlter bands. The system

standardized the use of color indices and helped lay

the groundwork for modern multi-band photometric

surveys. Even today, many modern photometric systems

are calibrated using Johnson’s original UBV ﬁlters or

derived from them.

3.2 The Historical Background and

Evolution

The concept of stellar magnitude dates back

to antiquity, when Greek astronomer Hipparchus

introduced a six-magnitude scale to classify the

brightness of visible stars. This logarithmic idea was

formalized in the 19th century when Norman Pogson

deﬁned a ﬁrst magnitude star as 100 times brighter than

a sixth magnitude star. However, this classiﬁcation was

based on visual observation and lacked instrumental

precision.

With the advent of photoelectric photometry in

the early 20th century, astronomers gained tools to

measure brightness with unprecedented accuracy. It

was in this context that Harold Johnson and William

Morgan developed the UBV photometric system. In

1953, they published a landmark paper introducing a

system based on three ﬁlters — U (ultraviolet), B (blue),

and V (visual) — centered around speciﬁc wavelength

bands. This was soon extended to include R (red) and I

(infrared) ﬁlters.

The Johnson system oﬀered a major advancement

in standardizing observations between observatories,

enabling large-scale comparison of stellar properties.

For the ﬁrst time, astronomers had a reproducible and

calibrated method for measuring and comparing stellar

magnitudes across diﬀerent wavelength regions.

3.3 Structure of the Johnson

Photometric System

The original Johnson system is characterized by a

set of broadband ﬁlters with well-deﬁned wavelength

ranges. These ﬁlters are designed to correspond

3.4 Applications of Johnson Magnitudes in Astrophysics

to diﬀerent parts of the electromagnetic spectrum,

capturing the continuum light of stars rather than

individual spectral lines. The most commonly used

ﬁlters include:

Filter Name Central λ Bandwidth

(nm) (nm)

U Ultraviolet ∼360 ∼60

B Blue ∼440 ∼100

V Visual (Green) ∼550 ∼90

R Red ∼700 ∼150

I Infrared ∼900 ∼150

Table 3.1: Broadband ﬁlters in the Johnson photometric

system.

The UBV system was the earliest and remains the

most frequently cited. The color indices derived from

this system, such as

(B − V )

and

(U − B)

, provide

valuable information about the temperature, reddening,

and spectral classiﬁcation of stars.

(B–V) Index: A measure of the star’s

temperature; hotter stars are bluer and have lower

(B − V ) values.

(U–B) Index: Sensitive to both temperature and

interstellar reddening.

These indices are diﬀerential magnitudes,

calculated by subtracting the magnitudes measured

in two ﬁlters:

(B − V ) = m

− m

(3.1)

where

and

are the magnitudes in the B

and V bands, respectively.

3.4 Applications of Johnson

Magnitudes in Astrophysics

3.4.1 Stellar Classiﬁcation and Temperature

Determination

The Johnson system plays a critical role in spectral

classiﬁcation of stars. The (B–V) color index is directly

correlated with the eﬀective temperature of a star.

This relationship enables astronomers to classify stars

along the Hertzsprung–Russell diagram, distinguishing

between main-sequence stars, giants, and white dwarfs.

For example:

O-type stars (very hot): (B − V ) ≈ −0.3

Sun-like stars (G-type): (B − V ) ≈ 0.65

Cool red stars (M-type): (B − V ) > 1.5

3.4.2 Extinction and Reddening Studies

Starlight traveling through interstellar dust is

absorbed and scattered, causing interstellar extinction

and a phenomenon known as reddening. Since dust

aﬀects shorter wavelengths more strongly, blue light

is absorbed more than red light, making stars appear

redder than they are.

By comparing observed color indices with intrinsic

(theoretical or known) indices, astronomers can estimate

the amount of dust between the star and Earth. The

color excess E(B − V ) is calculated as:

E(B − V ) = (B − V )

observed

− (B − V )

intrinsic

(3.2)

This value helps derive the visual extinction

often using:

= R

⋅ E(B − V ) (3.3)

with R

≈ 3.1 for the Milky Way.

3.4.3 Distance Measurements

Johnson magnitudes also support the

determination of distances through the distance

modulus:

m − M = 5 log

(d) − 5 + A

(3.4)

where:

m = apparent V magnitude

M = absolute V magnitude

d = distance in parsecs

= extinction in the V band

Thus, photometry in the Johnson system allows

astronomers to estimate distances to stars and stellar

clusters, provided extinction and intrinsic luminosity

are known.

3.5 Comparison with Other Photometric Systems

3.4.4 Color–Magnitude Diagrams (CMDs)

Color–Magnitude Diagrams (CMDs) are essential

tools in stellar population studies. Plotting the

magnitude against the

(B − V )

color, for example,

reveals the distribution of stars by brightness and

temperature. These diagrams are used to study:

Open and globular clusters,

Star formation history, and

Stellar evolution stages.

Because of the Johnson system’s widespread

adoption, a vast library of CMDs exists throughout

the literature for objects across the Galaxy and the

Local Group.

3.5 Comparison with Other

Photometric Systems

While the Johnson system was foundational, newer

photometric systems have been developed to suit

speciﬁc instruments and scientiﬁc objectives. Notable

examples include:

Cousins System (RI): Modiﬁed the redder ﬁlters

(

and

) to better match CCD detector response.

Str

omgren System: Utilizes narrow-band

ﬁlters for high-precision determination of stellar

parameters such as temperature, gravity, and

metallicity.

Sloan Digital Sky Survey (SDSS): Employs ﬁve

ﬁlters (

) optimized for modern CCDs

and large-area sky surveys.

Despite these advancements, the Johnson system

remains highly relevant. Many space- and ground-

based observations still rely on transformations between

modern photometric systems and Johnson UBVRI

magnitudes to maintain consistency and comparability

with historical datasets.

3.6 Limitations and Calibration

Challenges

Despite its strengths, the Johnson system has

several limitations:

Filter Variability: Diﬀerent observatories may

use slightly diﬀerent ﬁlter transmission curves,

introducing systematic errors in the measured

magnitudes.

CCD Incompatibility: Originally designed for

photoelectric photometers, Johnson ﬁlters are

not ideally matched to the spectral response of

modern CCD detectors.

Atmospheric Dependence: UV measurements,

particularly in the

band, are highly sensitive

to atmospheric extinction and therefore require

careful calibration.

To mitigate these issues, astronomers have

developed transformation equations and networks of

secondary standard stars to cross-calibrate data obtained

from diﬀerent instruments and photometric systems.

3.7 Legacy and Continuing Use

The Johnson photometric system’s legacy is visible

in almost every corner of stellar astrophysics. Its color

indices are still quoted in the latest catalogues, from

GAIA to 2MASS, and its ﬁlters remain a reference

point for calibration.

Moreover, the system provides a historical link

between older observations and modern surveys.

Many photometric databases, such as Simbad, Vizier,

and AAVSO, list Johnson magnitudes as baseline

photometric data.

In education, the Johnson system is often the

ﬁrst photometric system taught to students because

of its clarity, simplicity, and central role in stellar

astrophysics.

3.8 Conclusion

The development of the Johnson magnitude system

represented a transformative moment in astronomical

measurement. By providing a standardized, repeatable

method for quantifying stellar brightness across multiple

wavelengths, Johnson and Morgan ushered in a new

era of precision photometry. The system’s inﬂuence

continues to shape the way astronomers measure,

classify, and understand stars and galaxies.

3.8 Conclusion

While newer systems have evolved to meet the

demands of digital detectors and large-scale surveys, the

Johnson UBVRI ﬁlters remain deeply embedded in the

fabric of observational astronomy. For both historical

continuity and practical application, the Johnson system

continues to serve as a vital photometric standard — a

testament to its enduring utility in unveiling the cosmos.

References:

FUNDAMENTAL STELLAR PHOTOMETRY

FOR STANDARDS OF SPECTRAL TYPE ON

THE REVISED SYSTEM OF THE YERRES

SPECTRAL ATLAS

The Measurement of Starlight, Two Centuries of

Astronomical Photometry

INFRARED STELLAR PHOTOMETRY

UBV Photoelectric Photometry Catalogue (1986):

I. The Original data

About the Author

Sindhu G is a research scholar in the

Department of Physics at St. Thomas College,

Kozhencherry. She is doing research in Astronomy

& Astrophysics, with her work primarily focusing

on the classiﬁcation of variable stars using diﬀerent

machine learning algorithms. She is also involved

in period prediction for various types of variable

stars—especially eclipsing binaries—and in the study

of optical counterparts of X-ray binaries.

Part III

Biosciences

Protein Folding and its Vital Role in Biological

Function

by Geetha Paul

airis4D, Vol.3, No.8, 2025

www.airis4d.com

1.1 Introduction

Proteins are the molecular workhorses of all living

cells, responsible for various biological tasks, from

catalysing metabolic reactions to providing structural

support and transmitting signals. Yet, the functionality

of these remarkable molecules depends not merely on

their chemical composition, but critically on how they

fold into unique three-dimensional shapes. Protein

folding is the intricate process by which a chain of

amino acids, synthesised in a linear sequence according

to the instructions encoded in DNA, adopts a speciﬁc

conformation that allows it to perform its designated

function. This process is essential for life: every

movement, thought, and heartbeat relies on properly

folding proteins within the body.

The signiﬁcance of protein folding is underscored

by its central role in cellular biology. The journey from

gene to functional protein is more than a matter of

assembling the correct amino acids; it enables these

polymers to self-assemble into complex architectures,

precisely tailored for tasks as diverse as carrying oxygen,

repairing DNA, or defending against pathogens. This

intricate folding occurs under the crowded and dynamic

conditions typically found within cells. Despite the

potential for error, most proteins consistently fold into

their functional forms within seconds or minutes, a feat

made possible by the delicate balance of chemical and

physical forces acting along the polypeptide chain.

Disruptions in protein folding can have dramatic

consequences. Even a single error in this process

can transform a helpful protein into a harmful entity,

leading to the formation of aggregates that are hallmarks

of diseases like Alzheimer’s, Parkinson’s, and cystic

ﬁbrosis. Understanding how proteins fold, and what

happens when folding goes awry, is one of the grand

challenges of modern biology. Recent innovations in

research, ranging from uncovering the fundamental

principles that direct protein folding to advancing

computational approaches for structure prediction, are

having far-reaching impacts. These breakthroughs

are transforming medicine, enabling the development

of more precise drugs, and accelerating progress in

biotechnology, establishing protein folding as both a

cornerstone and a cutting-edge ﬁeld in modern science.

1.2 Translation and Primary

Structure Formation

Protein folding begins at the molecular level by

synthesising a polypeptide chain, a process called

translation. During this step, ribosomes read the

genetic code carried by messenger RNA (mRNA) and

link amino acids in a precise order, producing the

linear primary structure of the protein. This sequence

determines the intrinsic chemical properties of the future

protein by dictating how amino acids with diﬀerent side

chains interact. Hydrophilic, hydrophobic, acidic, or

basic will interact. Remarkably, the primary sequence

alone encodes all the information required for the

protein to adopt its native, functional structure. The

1.3 Folding to Secondary and Tertiary Structure: The Search for Native Conformation

growing protein chain may start folding into preliminary

structures even before synthesis is complete, a process

known as co-translational folding.

1.3

Folding to Secondary and Tertiary

Structure: The Search for Native

Conformation

As the newly synthesised chain is released from the

ribosome, it undergoes rapid structural transitions. The

ﬁrst level is the formation of local secondary structures,

alpha helices and beta sheets, stabilised by hydrogen

bonding patterns within the backbone. These early

structures form rapidly, providing a framework for

further folding.

The polypeptide chain then experiences a process

called hydrophobic collapse, where water-fearing

(hydrophobic) residues tuck inside, away from the

surrounding aqueous environment, leading to a compact

“molten globule” state. This intermediate is more

ordered than the unfolded chain but still ﬂexible.

Folding continues as further interactions occur between

distant regions of the chain, such as hydrophobic

contacts, salt bridges, disulﬁde bonds, and van der Waals

forces, driving the conversion into the tertiary structure,

which is the unique, three-dimensional form required for

biological activity. Proteins reach their functional state

usually by traversing an “energy landscape,” avoiding

misfolded intermediates or kinetic traps.

Some proteins, especially larger or more complex

ones, require assistance to fold correctly. Molecular

chaperones act as folding facilitators, preventing

improper contact and aggregation, particularly under

conditions prone to causing errors, such as heat shock

or cellular stress. Chaperones do not determine the ﬁnal

structure but provide an environment in which correct

folding can occur eﬃciently.

1.4 Quality Control, Quaternary

Structure Assembly, and

Functional Veriﬁcation

Once the tertiary structure is achieved, some

proteins interact with other polypeptide chains or

subunits, assembling into quaternary structures. They

are complexes that function as multimeric machines

(e.g., haemoglobin, which consists of four subunits).

Stability and correct assembly are ensured by the same

types of non-covalent interactions that govern earlier

folding stages.

Cellular machinery constantly monitors proteins

for incorrect or incomplete folding. Misfolded proteins

are recognised and tagged for degradation by quality-

control systems such as the ubiquitin-proteasome

pathway or autophagy. This process helps prevent the

accumulation of potentially toxic protein aggregates,

which are implicated in neurodegenerative and systemic

amyloid diseases if allowed to persist.

Thus, the journey from a linear amino acid

sequence to a ﬁnely tuned functional protein is a

tightly regulated, multi-step process essential to cellular

function and survival.

Protein aggregates are a hallmark of several

neurodegenerative diseases, such as Alzheimer’s,

Parkinson’s, and cystic ﬁbrosis, and play a central

role in their pathology. These aggregates typically

consist of misfolded proteins that have adopted

abnormal conformations rich in b-sheet structures. This

conformation exposes hydrophobic regions normally

buried inside the protein, promoting oligomerisation

and ﬁbril formation, which leads to insoluble deposits

in cells or extracellular spaces.

In Alzheimer’s disease, protein aggregates include

extracellular amyloid plaques formed mainly from

b-amyloid peptides and intracellular neuroﬁbrillary

tangles composed of hyperphosphorylated tau protein.

Parkinson’s disease is characterised by Lewy bodies,

intracellular aggregates primarily of a-synuclein protein.

Similarly, cystic ﬁbrosis involves misfolding and

aggregation of the CFTR protein, aﬀecting its function.

1.5 Mechanisms by Which Protein Aggregates Disrupt Cellular Function

Image courtesy: https://doi.org/10.1038/nature10317

Figure 1: Competing reactions of protein folding and

aggregation

Figure 2: Protein aggregation and its aﬀecting

mechanisms from molecule to molecule.

1.5 Mechanisms by Which Protein

Aggregates Disrupt Cellular

Function

Toxicity to neurons: Aggregates interfere

with cellular processes, causing oxidative stress,

mitochondrial dysfunction, and inﬂammation,

ultimately leading to cell death.

Impaired protein clearance: Dysfunction of

cellular quality control systems, such as the ubiquitin-

proteasome system and autophagy-lysosomal pathway,

contributes to the accumulation of aggregates. The

aggregated proteins can further impair these clearance

pathways, creating a vicious cycle.

Seeding and spreading: Aggregates can propagate

by inducing misfolding of normal proteins in

neighbouring cells, contributing to disease progression.

The causes of aggregation are multifactorial,

including genetic mutations, environmental stressors,

ageing, and failure of molecular chaperones and quality

control machinery. Deﬁciencies in protein homeostasis

(proteostasis) exacerbate the accumulation of toxic

aggregates, which are pathogenic in these diseases.

Understanding these aggregates is critical, as

their formation marks the onset and progression of

neurodegeneration and is a primary target for therapeutic

intervention.

References

Dobson, C. M. (2003). Protein folding and

misfolding. Nature, 426(6968), 884–890.

https://www.nature.com/articles/nature02261

Onuchic, J. N., & Wolynes, P. G. (2004). Theory

of protein folding. Current Opinion in Structural

Biology, 14(1), 70–75.

https://www.annualreviews.org/doi/10.1146/annurev.biophys.35.040405.102117

Hartl, F. U., Bracher, A., & Hayer-Hartl, M.

(2011). Molecular chaperones in protein folding

and proteostasis. Nature, 475(7356), 324–332.

https://www.nature.com/articles/nature10317

1.5 Mechanisms by Which Protein Aggregates Disrupt Cellular Function

Chiti, F., & Dobson, C. M. (2017).

Protein misfolding, amyloid formation,

and human disease: A summary of

progress over the last decade. Annual

Review of Biochemistry, 86, 27–68.

https://www.annualreviews.org/doi/10.1146/annurev-

biochem-061516-045115

About the Author

Geetha Paul is one of the directors of

airis4D. She leads the Biosciences Division. Her

research interests extends from Cell & Molecular

Biology to Environmental Sciences, Odonatology, and

Aquatic Biology.

About airis4D

Artiﬁcial Intelligence Research and Intelligent Systems (airis4D) is an AI and Bio-sciences Research Centre.

The Centre aims to create new knowledge in the ﬁeld of Space Science, Astronomy, Robotics, Agri Science,

Industry, and Biodiversity to bring Progress and Plenitude to the People and the Planet.

Vision

Humanity is in the 4th Industrial Revolution era, which operates on a cyber-physical production system. Cutting-

edge research and development in science and technology to create new knowledge and skills become the key to

the new world economy. Most of the resources for this goal can be harnessed by integrating biological systems

with intelligent computing systems oﬀered by AI. The future survival of humans, animals, and the ecosystem

depends on how eﬃciently the realities and resources are responsibly used for abundance and wellness. Artiﬁcial

intelligence Research and Intelligent Systems pursue this vision and look for the best actions that ensure an

abundant environment and ecosystem for the planet and the people.

Mission Statement

The 4D in airis4D represents the mission to Dream, Design, Develop, and Deploy Knowledge with the ﬁre of

commitment and dedication towards humanity and the ecosystem.

Dream

To promote the unlimited human potential to dream the impossible.

Design

To nurture the human capacity to articulate a dream and logically realise it.

Develop

To assist the talents to materialise a design into a product, a service, a knowledge that beneﬁts the community

and the planet.

Deploy

To realise and educate humanity that a knowledge that is not deployed makes no diﬀerence by its absence.

Campus

Situated in a lush green village campus in Thelliyoor, Kerala, India, airis4D was established under the auspicious

of SEED Foundation (Susthiratha, Environment, Education Development Foundation) a not-for-proﬁt company

for promoting Education, Research. Engineering, Biology, Development, etc.

The whole campus is powered by Solar power and has a rain harvesting facility to provide suﬃcient water supply

for up to three months of drought. The computing facility in the campus is accessible from anywhere through a

dedicated optical ﬁbre internet connectivity 24×7.

There is a freshwater stream that originates from the nearby hills and ﬂows through the middle of the campus.

The campus is a noted habitat for the biodiversity of tropical Fauna and Flora. airis4D carry out periodic and

systematic water quality and species diversity surveys in the region to ensure its richness. It is our pride that the

site has consistently been environment-friendly and rich in biodiversity. airis4D is also growing fruit plants that

can feed birds and provide water bodies to survive the drought.